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Multi-object tracking evaluated on sparse events

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Abstract

This article presents a visual object tracking method and applies an event-based performance evaluation metric for assessment. The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments. The tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, a performance evaluation method is presented and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These events are extracted automatically from the different trackers an their varying types of low level tracking results. Then, a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of multiple public datasets.

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References

  1. Aguilera J, Wildernauer H, Kampel M, Borg M, Thirde D, Ferryman J (2005) Evaluation of motion segmentation quality for aircraft activity surveillance. In: IEEE int workshop on VS-PETS, pp 293–300

  2. Bashir F, Porikli F (2006) Performance evaluation of object detection and tracking systems. In: IEEE international workshop on PETS, vol 5, pp 7–14

  3. Central pedestrian crossing: dataset. http://www.ee.ethz.ch/bleibe/data/datasets.html

  4. Chil project website. http://chil.server.de/

  5. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: CVPR

  6. Duizer P, Hansen D (2007) Multi-view video surveillance of outdoor traffic (master thesis). In: Digital project library, Aalborg University, Denmark

  7. Etiseo: video understanding evaluation. http://www.silogic.fr/etiseo

  8. Gonzlez J, Roca FX, Villanueva JJ (2007) Hermes: a research project on human sequence evaluation. In: Computational vision and medical image processing (VipIMAGE’2007)

  9. Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern 34:334–352

    Google Scholar 

  10. Lanz O (2006) Approximate bayesian multibody tracking. IEEE Trans Pattern Anal Mach Intell 28(9):1436–1449

    Article  Google Scholar 

  11. Leibe B, Schindler K, Van Gool L (2007) Coupled detection and trajectory estimation for multi-object tracking. In: International conference on computer vision (ICCV’07)

  12. Moeslund TB, Granum E (2001) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81(3):231–268

    Article  MATH  Google Scholar 

  13. Moeslund TB, Hilton A, Kruger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104:90–126

    Article  Google Scholar 

  14. Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. J Image Vis Comput 21(1):99–110

    Article  Google Scholar 

  15. Okuma K, Taleghani A, Freitas Nd, Littlei JJ, Lowe DG (2004) A boosted particle filter: multitarget detection and tracking

  16. Roth D, Doubek P, Van Gool L (2005) Bayesian pixel classification for human tracking. In: MOTION, pp 78–83

  17. Roth D, Koller-Meier E, Rowe D, Moeslund T, Van Gool L (2008) Event-based tracking evaluation metric. In: IEEE workshop on motion and video computing (WMVC)

  18. Rowe D, Reid I, Gonzlez J, Villanueva J (2006) Unconstrained multiple-people tracking. In: 28th DAGM. Springer LNCS, Berlin, pp 505–514

  19. Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: CVPR, pp 246–252

  20. Tsai R (1987) A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses. IEEE J Robot Autom 3(4):323–344

    Article  Google Scholar 

  21. Valera M, Velastin S (2005) Intelligent distributed surveillance systems: a review. IEE Proc Vis Image Signal Process 152(2):192–204

    Article  Google Scholar 

  22. Wu B, Nevatia R (2006) Tracking of multiple, partially occluded humans based on static body part detection. In: CVPR, pp 951–958

  23. Young D, Ferryman J (2005) Pets metrics: on-line performance evaluation service. In: Proc. 2nd joint IEEE int workshop on VS-PETS, pp 15–16

  24. Zhao T, Aggarwal M, Kumar R, Sawhney H (2005) Real-time wide area multi-camera stereo tracking. In: CVPR, pp 976–983

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Acknowledgements

The authors gratefully acknowledge support by the Swiss SNF NCCR project IM2 and EU project HERMES (FP6-027110). Furthermore, we would like to thank Prof. Dr. Thomas Moeslund from the University of Aalborg, Denmark and Dr. Jordi Gonzalez from the CVC Center in Barcelona, Spain for their tracking results and valuable input.

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Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, CH-8092, Zurich, Switzerland

    Daniel Roth, Esther Koller-Meier & Luc Van Gool

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  1. Daniel Roth
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  2. Esther Koller-Meier
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  3. Luc Van Gool
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Correspondence to Daniel Roth.

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Roth, D., Koller-Meier, E. & Van Gool, L. Multi-object tracking evaluated on sparse events. Multimed Tools Appl 50, 29–47 (2010). https://doi.org/10.1007/s11042-009-0365-x

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