Performance evaluation of crowd image analysis using the PETS2009 dataset

Lists

Download

Full text not archived in this repository.

Tools

Ferryman, J. and Ellis, A.-L. (2014) Performance evaluation of crowd image analysis using the PETS2009 dataset. Pattern Recognition Letters, 44. pp. 3-15. ISSN 0167-8655 doi: 10.1016/j.patrec.2014.01.005

Abstract/Summary

This paper presents the PETS2009 outdoor crowd image analysis surveillance dataset and the performance evaluation of people counting, detection and tracking results using the dataset submitted to five IEEE Performance Evaluation of Tracking and Surveillance (PETS) workshops. The evaluation was carried out using well established metrics developed in the Video Analysis and Content Extraction (VACE) programme and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The comparative evaluation highlights the detection and tracking performance of the authors’ systems in areas such as precision, accuracy and robustness and provides a brief analysis of the metrics themselves to provide further insights into the performance of the authors’ systems.

Altmetric Badge

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/36589
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Surveillance; Detection; Tracking; Performance evaluation; Dataset
Publisher	Elsevier
Download/View statistics	View download statistics for this item

Deposit Details

Date Deposited:	02 May 2014 08:17	Date item deposited into CentAUR
Last Modified:	21 Jul 2024 05:43	Date item last modified

References

A. Alahi, L. Jacques, Y. Boursier, P. Vandergheynst, Sparsity-driven people localization algorithm: evaluation in crowded scenes environment, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [2] A. Albiol, M.J. Sila, A. Albiol, J.M. Mossi, Video analysis using corners motion analysis, in: Proc. IEEE 11th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009, pp. 31–27, ISBN 978-07049-1501-4. [3] D. Arsic, A. Lyutskanov, G. Rigoll, B. Kwolek, Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [4] J. Badie, S. Bak, S.T. Serban, F. Bremond, Recovering people tracking errors using enhanced covariance-based signatures, in: Proc. IEEE 9th Int. Conf. Advanced Video and Signal-Based Surveillance (AVSS), 2012, pp. 487–493, http://dx.doi.org/10.1109/AVSS.2012.90. [5] J. Berclaz, F. Fleuret, P. Fua, Multiple object tracking using flow linear programming, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [6] D. Bolme, Y.M. Lui, B. Draper, J. Beveridge, Simple real-time human detection using a single correlation filter, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [7] M.D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, L. van Gool, Markovian tracking-by-detection from a single, uncalibrated camera, in: Proc. IEEE 11th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009, pp. 71–78, ISBN 978-07049-1501-4. [8] EC Funded CAVIAR project/IST 2001 37540: context aware vision using image based active recognition, <http://homepages.inf.ed.ac.uk/rbf/CAVIAR> (last accessed 01/03/2013). [9] A.B. Chan, M. Morrow, N. Vasconcelos, Analysis of crowded scenes using holistic properties, in: Proc. IEEE 11th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009, pp. 101–108, ISBN 978-07049-1501-4. [10] S. Choudri, J. Ferryman, A. Badii, Robust background model for pixel based people counting using a single uncalibrated camera, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [11] D. Conte, P. Foggia, G. Percannella, M. Vento, Performance evaluation of a people tracking system on the PETS video database, in: Proc. IEEE Advanced Video and Signal Based Surveillance (AVSS), 2010, http://dx.doi.org/10.1109/AVSS.2010.86. [12] D. Conte, P. Foggio, G. Percannella, M. Vento, A method based on the indirect approach for counting people in crowded scenes, in: Proc. IEEE Advanced Video and Signal Based Surveillance (AVSS), 2010, pp. 111–118, http://dx.doi.org/10.1109/AVSS.2010.86. [13] V. Eiselein, T. Senst, A motion-enhanced hybrid probability hypothesis density filter for real-time multi-human tracking in video surveillance, in: Proc. 15th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013, pp. 6–13, http://dx.doi.org/10.1109/PETS.2013.6523789. [14] M. Enzweiler, D.M. Gavrila Monocular pedestrian detection: survey and experiments IEEE Trans. Pattern Anal. Mach. Intell., 31 (12) (2009), pp. 2179–2195 View Record in Scopus | Full Text via CrossRef | Cited By in Scopus (307) [15] P. Foggia, G. Percannella, A. Saggese, M. Vento, Real-time tracking of single people and groups simultaneously by contextual graph-based reasoning dealing complex occlusions, in: Proc. 15th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013, pp. 29–36, http://dx.doi.org/10.1109/PETS.2013.6523792. [16] W. Ge, R. Collins, Evaluation of sampling-based pedestrian detection for crowd counting, in: Proc. IEEE 12th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), 2009, pp. 1–8, http://dx.doi.org/10.1109/PETS-WINTER.2009.5399487. [17] A. Heili, J.-M. Odobez, Parameter estimation and contextual adaption for multi-object tracking CRF model, in: Proc. 15th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013, pp. 14–21, http://dx.doi.org/10.1109/PETS.2013.6523790. [18] M. Hofmann, M. Haag, G. Rigoll, Unified hierarchical multi-object tracking using global data association, in: Proc. 15th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013, pp. 22–28, http://dx.doi.org/10.1109/PETS.2013.6523791. [19] R. Hossein, S. Velastin, P. Remagnino, T. Ellis, Vihasi: virtual human action silhouette data for the performance evaluation of silhouette-based action recognition methods, in: Proc. Second ACM/IEEE Intl Conf. Distributed Smart Cameras, 2008, p. 110, http://dx.doi.org/10.1109/ICDSC.2008.4635730. [20] Imagery Library for Intelligent Detection Systems, <http://www.ilids.co.uk> (last accessed: 29 January, 2012). [21] R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, R. Bowers, M. Boonstra, V. Korzhova, J. Zhang Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol IEEE Trans. Pattern Anal. Mach. Intell., 31 (2) (2009), pp. 319–336 View Record in Scopus | Full Text via CrossRef | Cited By in Scopus (118) [22] P. Morerio, L. Marcenaro, C.S. Regazzoni, People count estimation in small crowds, in: Proc. IEEE Advanced Video and Signal Based Surveillance (AVSS), 2012, pp. 476–480, http://dx.doi.org/10.1109/AVSS.2012.88. [23] A.T. Nghiem, F. Bremond, M. Thonnat, V. Valentin, ETISEO, performance evaluation for video surveillance systems, in: Proc. IEEE Int. Conf. Advanced Video and Signal Based Surveillance, 2007, pp 476–481. [24] W. Nie, A. Liu, Y. Su, Multiple person tracking by spatiotemporal tracklet association, in: Proc. IEEE 9th Int. Conf. Advanced Video and Signal-Based Surveillance (AVSS), 2012, pp. 487–493, http://dx.doi.org/10.1109/AVSS.2012.89. [25] S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C-C. Chen, J.T. Lee, S. Mukherjee, J.K. Aggarwal, H. Lee, L. Davis, E. Swears, X. Wang, Q. Ji, K. Reddy, M. Shah, C. Vondrick, H. Pirsiavash, D. Ramanan, J. Yuen, A. Torralba, B. Song, A. Fong, A. Roy-Chowdhury, M. Desai, A large-scale benchmark dataset for event recognition in surveillance video, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 3153–3160, http://dx.doi.org/10.1109/CVPR.2011.5995586. [26] M. Paetzold, T. Sikora, Counting people in crowded environments by fusion of shape and motion information, in: Proc. IEEE Advanced Video and Signal Based Surveillance (AVSS), 2010, pp. 157–164, http://dx.doi.org/10.1109/AVSS.2010.92. [27] PETS2006, Ninth IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, USA, June 18, 2006, ISBN 0-7049-1422-0. [28] PETS2007, Tenth IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Rio De Janeiro, October 14, 2007, ISBN 0-7049-1423-9. [29] P.K. Sharma, C. Huang, R. Nevatia, Evaluation of people tracking, counting and density estimation in crowded environments, in: Proc. IEEE 11th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 39–46, ISBN 978-07049-1501-4. [30] S. Singh, S. Velastin, R. Hossein, Muhavi: a multicamera human action video dataset for the evaluation of action recognition methods, in: Proc. Seventh IEEE Intl Conf. Advanced Video and Signal Based Surveillance, 2010, http://dx.doi.org/10.1109/AVSS.2010.63. [31] V.B. Subburaman, A. Descamps, C. Carincotte, Counting people in the crowd using a generic head counter, in: Proc. IEEE 9th Int. Conf. Advanced Video and Signal-Based Surveillance (AVSS), 2012, pp. 470–475, http://dx.doi.org/10.1109/AVSS.2012.87. [32] <http://trecvid.nist.gov/> (last accessed: 01/03/2013). [33] T. Xu, P. Peng, X. Fang, C. Su, Y. Wang, Y. Tian, W. Zeng, T. Huang, Single and multiple view detection, tracking and video analysis in crowded environments, in: Proc. IEEE 9th Int. Conf. Advanced Video and Signal-Based Surveillance (AVSS), pp. 494–499, http://dx.doi.org/10.1109/AVSS.2012.91. [34] J. Yang, Z. Shi, Probabilistic multiple people tracking through complex situations, in: Proc. IEEE 11th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009, pp. 79–86, ISBN 978-07049-1501-4. [35] A. Zweng, M. Kampel, Performance evaluation of an improved relational feature model for pedestrian detection, in Proc. 15th Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2013, pp. 53–60, http://dx.doi.org/10.1109/PETS.2013.6523795.

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar