Multi-object tracking evaluated on sparse events

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.

[1]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[2]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  L. Van Gool,et al.  Event-Based Tracking Evaluation Metric , 2008, 2008 IEEE Workshop on Motion and video Computing.

[4]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[5]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[6]  Oswald Lanz,et al.  Approximate Bayesian multibody tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[8]  Harpreet S. Sawhney,et al.  Real-time wide area multi-camera stereo tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Luc Van Gool,et al.  Bayesian Pixel Classification for Human Tracking , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[10]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[11]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[12]  Fatih Porikli,et al.  Performance Evaluation of Object Detection and Tracking Systems , 2006 .

[13]  Ian D. Reid,et al.  Unconstrained Multiple-People Tracking , 2006, DAGM-Symposium.

[14]  Luc Van Gool,et al.  Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Sergio A. Velastin,et al.  Intelligent distributed surveillance systems: a review , 2005 .

[16]  D. Thirde,et al.  Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[17]  Ramakant Nevatia,et al.  Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  J.M. Ferryman,et al.  PETS Metrics: On-Line Performance Evaluation Service , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[19]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  Jordi Gonzàlez,et al.  HERMES: A research project on human sequence evaluation , 2007 .