Publications

@inproceedings{Krzeszowski2023,

title = {3D Tracking of Multiple Drones Based on Particle Swarm Optimization},

author = { Tomasz Krzeszowski and Adam Switonski and Michal Zielinski and Konrad Wojciechowski and Jakub Rosner},

editor = { Ji{ř}í Mikyška and Clélia de Mulatier and Maciej Paszynski and Valeria V. Krzhizhanovskaya and Jack J. Dongarra and Peter M.A. Sloot},

url = {https://doi.org/10.1007/978-3-031-36027-5_18},

doi = {10.1007/978-3-031-36027-5_18},

isbn = {978-3-031-36027-5},

year  = {2023},

date = {2023-01-01},

booktitle = {Computational Science – ICCS 2023},

volume = {10476},

pages = {245–258},

publisher = {Springer Nature Switzerland},

abstract = {This paper presents a method for the tracking of multiple drones in three-dimensional space based on data from a multi-camera system. It uses the Particle Swarm Optimization (PSO) algorithm and methods for background/foreground detection. In order to evaluate the developed tracking algorithm, the dataset consisting of three simulation sequences and two real ones was prepared. The sequences contain from one to ten drones moving with different flight patterns. The simulation sequences were created using the Unreal Engine and the AirSim plugin, whereas the real sequences were registered in the Human Motion Lab at the Polish-Japanese Academy of Information Technology. The lab is equipped with the Vicon motion capture system, which was used to acquire ground truth data. The conducted experiments show the high efficiency and accuracy of the proposed method. For the simulation data, tracking errors from 0.086 m to 0.197 m were obtained, while for real data, the error was 0.101–0.124 m. The system was developed for augmented reality applications, especially games. The dataset is available at http://bytom.pja.edu.pl/drones/.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wubben2019DS-RT,

title = {A vision-based system for autonomous vertical landing of unmanned aerial vehicles},

author = { J. {Wubben} and F. {Fabra} and C. T. {Calafate} and T. {Krzeszowski} and J. M. {Marquez-Barja} and J. {Cano} and P. {Manzoni}},

doi = {10.1109/DS-RT47707.2019.8958701},

issn = {1550-6525},

year  = {2019},

date = {2019-10-01},

booktitle = {2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},

pages = {1-7},

abstract = {Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPS-based landing, that typically deviates from the intended target by 1 to 3 meters.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KrzeszowskiSSRTS2019,

title = {The Application of Multiview Human Body Tracking on the Example of Hurdle Clearance},

author = { Tomasz 

and Przednowek Krzysztof 

and Wiktorowicz Krzysztof 

and Iskra Janusz Krzeszowski},

editor = { Jan 

and Pezarat-Correia Pedro 

and Vilas-Boas Jo{ã}o Cabri},

url = {https://link.springer.com/chapter/10.1007/978-3-030-14526-2_8},

doi = {10.1007/978-3-030-14526-2_8},

isbn = {978-3-030-14526-2},

year  = {2019},

date = {2019-01-01},

booktitle = {Sport Science Research and Technology Support},

volume = {975},

pages = {116–127},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {This initial research presents the multiview human body tracking method as a tool to measure hurdle clearance parameters. This study was conducted on high level hurdlers, who were members of the Polish national team. The video sequences were recorded by a multicamera system consisting of three 100 Hz Full HD cameras. The sequences were registered under the simulated starting conditions of a 110 m hurdles race. Kinematic parameters were estimated based on the analysis of images from the multicamera system. These parameters were compared with the parameters obtained from ground truth poses. Mean absolute error and mean relative error were selected as the quality criteria. The main advantage of the method presented here is that it does not need any special clothes, markers or the support of other estimation techniques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rymut2018,

title = {Kinematic Analysis of Hurdle Clearance using a Mobile Device},

author = { Boguslaw Rymut and Tomasz Krzeszowski and Krzysztof Przednowek and Karolina H Przednowek and Janusz Iskra},

url = {http://dx.doi.org/10.5220/0006933600490055},

doi = {10.5220/0006933600490055},

isbn = {978-989-758-325-4},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 6th International Congress on Sport Sciences Research and Technology Support - Volume 1 (icSPORTS 2018)},

pages = {49–55},

publisher = {Scitepress},

address = {Seville, Spain},

abstract = {This paper presents a human motion tracking method using a mobile device. The proposed method may be used as a tool to measure hurdle clearance kinematic parameters and help coaches to evaluate the athlete's technique. The video recordings were made under simulated starting conditions of a 100 m women hurdle race. Kinematic parameters were estimated based on an analysis of images sequence from a mobile device. The images were recorded on a HTC M8s smartphone with a resolution of 1920x1080 pixels and with a frequency of 30 Hz. The system was tested on two mobile development platforms and three image sequences. The proposed method does not use any markers, special clothes or other estimation support techniques. The analysis conducted showed that the smallest errors were calculated for the height of centre of mass, while the biggest errors were observed for the bending angle of the knee of the trail leg.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2018,

title = {Comparison of selected fuzzy PSO algorithms},

author = { Tomasz Krzeszowski and Krzysztof Wiktorowicz and Krzysztof Przednowek},

url = {https://link.springer.com/chapter/10.1007/978-3-319-59861-1_7},

doi = {10.1007/978-3-319-59861-1_7},

isbn = {978-3-319-59860-4},

year  = {2018},

date = {2018-01-01},

booktitle = {Recent Advances in Computational Optimization, Studies in Computational Intelligence},

volume = {717},

pages = {107–122},

publisher = {Springer Cham},

abstract = {This paper presents a comparison of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi–Sugeno fuzzy system is used to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions frequently used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function final value.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Przednowek2018b,

title = {Mobile application for predictive modelling in hurdles race},

author = { K Przednowek and K Wiktorowicz and T Krzeszowski and M Tumidajewicz and J Iskra},

url = {https://doi.org/10.1109/TISHW.2018.8559575},

doi = {10.1109/TISHW.2018.8559575},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW)},

pages = {1–7},

publisher = {IEEE},

address = {Thessaloniki, Greece},

abstract = {This paper presents a mobile expert system for Android platform, named R-tificial Trainer, to support the work of a hurdles coach in planning training programmes. The main feature of the developed application is the ability to generate training loads and predict results for an athlete. It includes a database of players and allows the user to generate training plan in PDF format. The application has been tested on a dataset of athletes practising hurdles on the 110 metres. The database contains 120 training programmes made by 18 athletes. The application uses the Predictive Model Markup Language standard. The predictive models include linear models in the form of ordinary least squares and LASSO regressions and nonlinear model in the form of a multilayer perceptron with exponential function. To choose the best method, the leave-one-out cross-validation is used. The lowest validation error was achieved by multilayer perceptron.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2017,

title = {Multiview Human Body Tracking of Hurdle Clearance: A Case Study},

author = { Tomasz Krzeszowski and Krzysztof Przednowek and Krzysztof Wiktorowicz and Janusz Iskra},

url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006498400830088},

doi = {10.5220/0006498400830088},

isbn = {978-989-758-269-1},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support},

pages = {83–88},

publisher = {Scitepress},

address = {Funchal, Madeira, Portugal},

abstract = {This initial research is a case study that uses a multiview human body tracking method as a tool to measure hurdle clearance kinematic parameters. This study is conducted on a hurdler representing a high sport level, who is a participant in the European and World Championships and the Olympic Games. The video recordings were made under simulated starting conditions of a 110 m hurdle race. Kinematic parameters are estimated based on the analysis of images from a multicamera system. The images were recorded with a resolution of 1920x1080 and with a frequency of 100 Hz. The proposed method does not use any special clothes, markers or other estimation support techniques. The parameters of the hurdle clearance were compared with the parameters obtained from ground truth poses. Mean Absolute Error and Mean Relative Error were used as the quality criteria.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Przednowek2017a,

title = {Application of Artificial Neural Models for Planning Sport Training in 110m Hurdles},

author = { Krzysztof Przednowek and Janusz Iskra and Tomasz Krzeszowski and Karolina H Przednowek},

url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=YNVIq8UwnRE=&t=1},

doi = {10.5220/0006499400410046},

isbn = {9789897582707},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics},

pages = {41–46},

publisher = {Scitepress},

address = {Funchal, Madeira, Portugal},

abstract = {This paper presents the use of artificial neural networks for planning sport training in 110 meters hurdles. The model was calculated based on the training data of the Polish National Team hurdlers. The analysis was based on 120 training plans that represent a different period in the annual training cycle. The MLP and RBF networks were used in this study as a predictive model. The neural network developed has 6 inputs representing the parameters of the athlete and 15 outputs representing the training loads. To evaluate the model, a set of 20 records were used. The smallest prediction error was obtained for the multilayer perceptron with 9 neurons in the hidden layer and a hyperbolic tangent as the activation functions. The resulting model may be used as a tool to assist coaches in planning training loads during the selected training period.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iskra2017a,

title = {Kinematic Analysis of the Upper Limbs in Stepping over the Hurdle - The Use of IMU-based Motion Capture},

author = { Janusz Iskra and Krzysztof Przednowek and Tomasz Krzeszowski and Krzysztof Wiktorowicz and Michal Pietrzak},

url = {http://www.scitepress.org/PublicationsDetail.aspx?ID=F018sAN2qE4=&t=1},

doi = {10.5220/0006503101020106},

isbn = {9789897582691},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support},

pages = {102–106},

publisher = {Scitepress},

address = {Funchal, Madeira, Portugal},

abstract = {This paper presents an analysis of the kinematic parameters of the upper limbs in stepping over the hurdle. Stepping over the hurdle is a specific exercise practised throughout the year. In this exercise, three key points were analysed in take-off, flight and landing phases. The aim of the study was to use the IMU-based (inertial measurement unit) motion capture system to evaluate the movement of the hurdlers' upper limbs while stepping over the hurdle using both the better leg, and the worse leg. The sequences were obtained using 18 sensors working at a frequency of 120 Hz. The analysis was made using two high-achieving athletes. This paper presents the linear velocities and the trajectory of selected segments of the upper limbs. In most cases the velocities of the segments were higher for the better leg. The analysis shows that during the specific exercise of stepping over the hurdle attention should be paid to the movement of the trail arm in the landing phase.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2016a,

title = {Evaluation of selected fuzzy particle swarm optimization algorithms},

author = { T. Krzeszowski and K. Wiktorowicz},

url = {https://ieeexplore.ieee.org/document/7733295},

doi = {10.15439/2016F206},

isbn = {9788360810903},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016},

publisher = {IEEE},

address = {Gdansk, Poland},

abstract = {This paper is devoted to an evaluation of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi-Sugeno fuzzy system is utilized to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed, in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function and the relative success.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Przednowek2016,

title = {A fuzzy-based software tool used to predict 110m hurdles results during the annual training cycle},

author = { Krzysztof Przednowek and Krzysztof Wiktorowicz and Tomasz Krzeszowski and Janusz Iskra},

editor = { Pedro Pezarat Correia and Jan Cabri},

url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006043701760181},

doi = {10.5220/0006043701760181},

isbn = {9789897582059},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2016)},

pages = {176–181},

publisher = {Scitepress},

address = {Porto, Portugal},

abstract = {This paper describes a fuzzy-based software tool for predicting results in the 110m hurdles. The predictive models were built on using 40 annual training cycles completed by 18 athletes. These models include: ordinary least squares regression, ridge regression, LASSO regression, elastic net regression and nonlinear fuzzy correction of least squares regression. In order to compare them, and choose the best model, leave-one-out cross-validation was used. This showed that the fuzzy corrector proposed in this paper has the lowest prediction error. The developed software can support a coach in planning an athlete's annual training cycle. It allows the athlete's results to be predicted, and in this way, for the best training loads to be selected. The tool is a web-based interactive application that can be run from a computer or a mobile device. The whole system was implemented using the R programming language with additional packages.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iskra2016,

title = {Evaluation of Kinematic Parameters of Hurdles Clearance During Fatigue in Men's 400m Hurdles — Research Using the Method of Computer Vision},

author = { Krzysztof Przednowek and Janusz Iskra and Tomasz Krzeszowski and Krzysztof Wiktorowicz},

editor = { Kajetan J. Slomka and Grzegorz Juras},

year  = {2016},

date = {2016-01-01},

booktitle = {Current research in motor control V: bridging motor control and biomechanics},

pages = {232–238},

publisher = {AWF Katowice},

address = {Katowice},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lenik2015,

title = {The Analysis of Basketball Free Throw Trajectory using PSO Algorithm},

author = { Pawel Lenik and Tomasz Krzeszowski and Krzysztof Przednowek and Justyna Lenik},

url = {http://dx.doi.org/10.5220/0005611002500256},

doi = {10.5220/0005611002500256},

isbn = {978-989-758-159-5},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support (icSPORTS 2015)},

pages = {250–256},

publisher = {Scitepress},

abstract = {The following paper described the method for automatic measurement of selected parameters of a basketball free throw trajectory. The research material was based on 10 sequences recorded by a monocular camera. For tracking the ball the particle swarm optimization (PSO) algorithm was used. Additionally the method of ball detection was developed. The study was conducted on a group of 10 basketball players who participated in the Polish Second Division during the 2014/2015 season. The 10 parameters (four distances, three velocities, and three angle parameters) were taken into account. The experimental results showed that the value of the initial angle was equal to 47.27±4.42 degrees, and the height of ball trajectory was at the level of 3.84±0.34 m. The correlation between body height and parameter of a free throw was also determined. The analysis conducted showed a significant correlation between the height and shape of a free throw trajectory. The suggested method can be used in the training process as a tool to improve performance of the free throw.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kwolek2014,

title = {3D gait recognition using spatio-temporal motion descriptors},

author = { Bogdan Kwolek and Tomasz Krzeszowski and Agnieszka Michalczuk and Henryk Josinski},

url = {http://dx.doi.org/10.1007/978-3-319-05458-2_61},

doi = {10.1007/978-3-319-05458-2_61},

isbn = {978-3-319-05457-5},

year  = {2014},

date = {2014-01-01},

booktitle = {The 6th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2014)},

volume = {8398 LNCS},

pages = {595–604},

publisher = {Springer Cham},

abstract = {We present a view independent algorithm for 3D human gait recognition. The identification of the person is achieved using motion data obtained by our markerless 3D motion tracking algorithm. We report its tracking accuracy using ground-truth data obtained by a marker-based motion capture system. The classification is done using SVM built on the proposed spatio-temporal motion descriptors. The identification performance was determined using 230 gait cycles performed by 22 persons. The correctly classified ratio achieved by SVM is equal to 93.5% for rank 1 and 99.6% for rank 3. We show that the recognition performance obtained with the spatio-temporal gait signatures is better in comparison to accuracy obtained with tensorial gait data and reduced by MPCA.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Przednowek2014,

title = {Markerless Motion Tracking in Evaluation of Hurdle Clearance Parameters},

author = { Krzysztof Przednowek and Tomasz Krzeszowski and Janusz Iskra and Krzysztof Wiktorowicz},

url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005080601290136},

doi = {10.5220/0005080601290136},

isbn = {978-989-758-057-4},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 2nd International Congress on Sports Sciences Research and Technology Support (icSPORTS-2014)},

pages = {129–136},

publisher = {Scitepress},

abstract = {In this study, implementation of markerless method of human body motion tracking as a tool of measurement of hurdle clearance kinematic parameters was presented. The analysis involved 5 hurdle runners at various training levels. Recording of video sequences was carried out under simulated starting conditions of a 110 m hurdle race. Kinematic parameters were determined based on the analysis of images recorded with a 100 Hz monocular camera. The suggested method does not involve using any special clothes, markers or estimation support techniques. In the study, the basic numerical characteristics of twenty estimated parameters were presented. The accuracy of determined hurdle clearance parameters was verified by comparison of estimated poses with the ground truth pose. As the quality criterion, the MAE (Mean Absolute Error) was adopted. In the distance parameters, the least error was obtained for the distance between the center of mass (CM) and the hurdle at the first hurdle clearance phase (MAE = 22.0 mm). For the angular parameters, the least error was obtained for the leg angle at the first hurdle clearance phase (MAE = 3.1°). The level of computed errors showed that the presented method can be used for estimation of hurdle clearance kinematic parameters.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2014,

title = {DTW-based gait recognition from recovered 3-D joint angles and inter-ankle distance},

author = { Tomasz Krzeszowski and Adam Switonski and Bogdan Kwolek and Henryk Josinski and Konrad Wojciechowski},

url = {http://dx.doi.org/10.1007/978-3-319-11331-9_43},

doi = {10.1007/978-3-319-11331-9_43},

isbn = {978-3-319-11330-2},

year  = {2014},

date = {2014-01-01},

booktitle = {Int. Conf. on Computer Vision and Graphics 2014 (ICCVG 2014), LNCS},

volume = {8671},

pages = {356–363},

publisher = {Springer Cham},

abstract = {We present a view independent approach for 3D human gait recognition. The identification of the person is done on the basis of motion estimated by our marker-less 3D motion tracking algorithm. We show tracking performance using ground-truth data acquired by Vicon motion capture system. The identification is achieved by dynamic time warping using both joint angles and inter-joint distances. We show how to calculate approximate Euclidean distance metric between two sets of Euler angles. We compare the correctly classified ratio obtained by DTW built on unit quaternion distance metric and such an Euler angle distance metric. We then show that combining the rotation distances with inter-ankle distances and other person attributes like height leads to considerably better correctly classified ratio.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rymut2013,

title = {GPU-accelerated human motion tracking using particle filter combined with PSO},

author = { Boguslaw Rymut and Bogdan Kwolek and Tomasz Krzeszowski},

url = {http://dx.doi.org/10.1007/978-3-319-02895-8_38},

doi = {10.1007/978-3-319-02895-8_38},

isbn = {978-3-319-02894-1},

year  = {2013},

date = {2013-01-01},

booktitle = {Advanced Concepts for Intelligent Vision Systems. ACIVS 2013},

volume = {8192 LNCS},

pages = {426–437},

publisher = {Springer Cham},

abstract = {This paper discusses how to combine particle filter (PF) with particle swarm optimization (PSO) to achieve better object tracking. Owing to multi-swarm based mode seeking the algorithm is capable of maintaining multimodal probability distributions and the tracking accuracy is far better than accuracy of PF or PSO. We propose parallel resampling scheme for particle filtering running on GPU. We show the efficiency of the parallel PF-PSO algorithm on 3D model based human motion tracking. The 3D model is rasterized in parallel and single thread processes one column of the image. Such level of parallelism allows us to efficiently utilize the GPU resources and to perform tracking of the full human body at rates of 15 frames per second. The GPU achieves an average speedup of 7.5 over the CPU. For marker-less motion capture system consisting of four calibrated cameras, the computations were conducted on four CPU cores and four GTX GPUs on two cards.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2013,

title = {Gait recognition based on marker-less 3D motion capture},

author = { Tomasz Krzeszowski and Agnieszka Michalczuk and Bogdan Kwolek and Adam Switonski and Henryk Josinski},

url = {http://dx.doi.org/10.1109/AVSS.2013.6636645},

doi = {10.1109/AVSS.2013.6636645},

isbn = {978-1-4799-0703-8},

year  = {2013},

date = {2013-01-01},

booktitle = {10th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance 2013 (AVSS 2013)},

pages = {232–237},

publisher = {IEEE},

abstract = {We present an algorithm for view-independent gait-based person identification. The identification is achieved using data obtained by our marker-less 3D motion tracking algorithm. The motion tracking was accomplished by a particle swarm optimization algorithm. The accuracy of the motion tracking algorithm was evaluated using ground-truth data from MoCap. It was determined on 88 sequences with 22 walking performers. We obtained 90% identification accuracy (rank 1) on 230 gait cycles.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2012,

title = {View independent human gait recognition using markerless 3d human motion capture},

author = { Tomasz Krzeszowski and Bogdan Kwolek and Agnieszka Michalczuk and Adam Switonski and Henryk Josinski},

url = {http://dx.doi.org/10.1007/978-3-642-33564-8_59},

doi = {10.1007/978-3-642-33564-8_59},

issn = {0302-9743},

year  = {2012},

date = {2012-01-01},

booktitle = {Int. Conf. on Computer Vision and Graphics 2012 (ICCVG 2012)},

volume = {7594 LNCS},

pages = {491–500},

publisher = {Springer Berlin Heidelberg},

abstract = {We present an algorithm for view-independent human gait recognition. The human gait recognition is achieved using data obtained by our markerless 3D motion tracking algorithm. The tensorial gait data were reduced by multilinear principal component analysis and subsequently classified. The performance of the motion tracking algorithm was evaluated using ground-truth data from MoCap. The classification accuracy was determined using video sequences with walking performers. Experiments on multiview video sequences show the promising effectiveness of the proposed algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kwolek2012,

title = {Real-time multi-view human motion tracking using particle swarm optimization with resampling},

author = { B. Kwolek and T. Krzeszowski and A. Gagalowicz and K. Wojciechowski and H. Josinski},

url = {http://dx.doi.org/10.1007/978-3-642-31567-1_9},

doi = {10.1007/978-3-642-31567-1_9},

isbn = {978-3-642-31566-4},

year  = {2012},

date = {2012-01-01},

booktitle = {7th Int. Conf. on Articulated Motion and Deformable Objects 2012},

volume = {7378 LNCS},

pages = {92–101},

publisher = {Springer Berlin Heidelberg},

abstract = {In this paper we propose a particle swarm optimization with resampling for marker-less body tracking. The resampling is employed to select a record of the best particles according to the weights of particles making up the swarm. The algorithm better copes with noise and reduces the premature stagnation. Experiments on 4-camera datasets show the robustness and accuracy of our method. It was evaluated on nine sequences using ground truth provided by Vicon. The full body motion tracking was conducted in real-time on two PC nodes, each of them with two multi-core CPUs with hyper-threading, connected by 1 GigE.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2012a,

title = {Real-time tracking of full-body motion using parallel particle swarm optimization with a pool of best particles},

author = { Tomasz Krzeszowski and Bogdan Kwolek and Boguslaw Rymut and Konrad Wojciechowski and Henryk Josinski},

url = {http://dx.doi.org/10.1007/978-3-642-29353-5_12},

doi = {10.1007/978-3-642-29353-5_12},

isbn = {978-3-642-29352-8},

year  = {2012},

date = {2012-01-01},

booktitle = {Int. Conf. on Artificial Intelligence and Soft Computing 2012, Swarm and Evolutionary Computation},

volume = {7269 LNCS},

pages = {102–109},

publisher = {Springer Berlin Heidelberg},

abstract = {In this paper we present a particle swarm optimization (PSO) based approach for marker-less full body motion tracking. The objective function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool to force the PSO jump out of stagnation. Experiments on 4-camera datasets demonstrate the robustness and accuracy of our method. The tracking is conducted on 2 PC nodes with multi-core CPUs, connected by 1 GigE. This makes our system capable of accurately recovering full body movements with 14 fps.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rymut2012,

title = {Full body motion tracking in monocular images using particle swarm optimization},

author = { Boguslaw Rymut and Tomasz Krzeszowski and Bogdan Kwolek},

url = {http://dx.doi.org/10.1007/978-3-642-29347-4_70},

doi = {10.1007/978-3-642-29347-4_70},

isbn = {978-3-642-29346-7},

year  = {2012},

date = {2012-01-01},

booktitle = {Int. Conf. on Artificial Intelligence and Soft Computing 2012, Artificial Intelligence and Soft Computing},

volume = {7267 LNAI},

number = {PART 1},

pages = {600–607},

publisher = {Springer Berlin Heidelberg},

abstract = {The estimation of full body pose in monocular images is a very difficult problem. In 3D-model based motion tracking the challenges arise as at least one-third of degrees of freedom of the human pose that needs to be recovered is nearly unobservable in any given monocular image. In this paper, we deal with high dimensionality of the search space through estimating the pose in a hierarchical manner using Particle Swarm Optimization. Our method fits the projected body parts of an articulated model to detected body parts at color images with support of edge distance transform. The algorithm was evaluated quantitatively through the use of the motion capture data as ground truth. © 2012 Springer-Verlag Berlin Heidelberg.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kwolek2011,

title = {Real-time multi-view human motion tracking using 3D model and latency tolerant parallel particle swarm optimization},

author = { Bogdan Kwolek and Tomasz Krzeszowski and Konrad Wojciechowski},

url = {http://dx.doi.org/10.1007/978-3-642-24136-9_15},

doi = {10.1007/978-3-642-24136-9_15},

isbn = {978-3-642-24135-2},

year  = {2011},

date = {2011-01-01},

booktitle = {Int. Conf. on Computer Vision/Computer Graphics Collaboration Techniques "MIRAGE 2011"},

volume = {6930 LNCS},

pages = {169–180},

publisher = {Springer Berlin Heidelberg},

abstract = {This paper demonstrates how latency tolerant parallel particle swarm optimization can be used to achieve real-time full-body motion tracking. The tracking is realized using multi-view images and articulated 3D model with a truncated cones-based representation of the body. Each CPU core computes fitness score for a single camera. On each node the algorithm uses the current temporary best fitness value without waiting for the global best one from cooperating sub-swarms. The algorithm runs at 10 Hz on eight PC nodes connected by 1 GigE.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kwolek2011a,

title = {Swarm intelligence based searching schemes for articulated 3D body motion tracking},

author = { Bogdan Kwolek and Tomasz Krzeszowski and Konrad Wojciechowski},

url = {http://dx.doi.org/10.1007/978-3-642-23687-7_11},

doi = {10.1007/978-3-642-23687-7_11},

isbn = {978-3-642-23686-0},

year  = {2011},

date = {2011-01-01},

booktitle = {Advanced Concepts for Intelligent Vision Systems 2011},

volume = {6915 LNCS},

pages = {115–126},

publisher = {Springer Berlin Heidelberg},

abstract = {We investigate swarm intelligence based searching schemes for effective articulated human body tracking. The fitness function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool. We propose a global-local annealed particle swarm optimization to alleviate the inconsistencies between the observed human pose and the estimated configuration of the 3D model. At the beginning of each optimization cycle, estimation of the pose of the whole body takes place and then the limb poses are refined locally using smaller number of particles. The investigated searching schemes were compared by analyses carried out both through qualitative visual evaluations as well as quantitatively through the use of the motion capture data as ground truth. The experimental results show that our algorithm outperforms the other swarm intelligence searching schemes. The images were captured using multi-camera system consisting of calibrated and synchronized cameras. © 2011 Springer-Verlag.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krzeszowski2010a,

title = {GPU-accelerated tracking of the motion of 3D articulated figure},

author = { T. Krzeszowski and B. Kwolek and K. Wojciechowski},

url = {http://dx.doi.org/10.1007/978-3-642-15910-7_18},

doi = {10.1007/978-3-642-15910-7_18},

isbn = {978-3-642-15909-1},

year  = {2010},

date = {2010-01-01},

booktitle = {Int. Conf. on Computer Vision and Graphics 2010},

volume = {6374 LNCS},

number = {PART 1},

pages = {155–162},

publisher = {Springer Berlin Heidelberg},

abstract = {This paper presents methods that utilize the advantages of modern graphics card hardware for real-time full body tracking with a 3D body model. By means of the presented methods the tracking of full body can be performed at frame-rates of 5 frames per second using a single low-cost moderately-priced graphics card and images from single camera. For a model with 26 DOF we achieved 15 times speed-up. The pose configuration is given by the position and orientation of the pelvis as well as relative joint angles between the connected limbs. The tracking is done through searching for a model configuration that best corresponds to the observed human silhouette in the input image. The searching is done via particle swarm optimization, where each particle corresponds to some hypothesized set of model parameters. © 2010 Springer-Verlag Berlin Heidelberg.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}