An Appearance-Based Body Model for Multiple People Tracking
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Optical Flow for Person Tracking
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Logic-based trajectory evaluation in videos
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
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Performance evaluation of object tracking systems is typically performed after the data has been processed, by comparing tracking results to ground truth. Whilst this approach is fine when performing offline testing, it does not allow for real-time analysis of the systems performance, which may be of use for live systems to either automatically tune the system or report reliability. In this paper, we propose three metrics that can be used to dynamically asses the performance of an object tracking system. Outputs and results from various stages in the tracking system are used to obtain measures that indicate the performance of motion segmentation, object detection and object matching. The proposed dynamic metrics are shown to accurately indicate tracking errors when visually comparing metric results to tracking output, and are shown to display similar trends to the ETISEO metrics when comparing different tracking configurations.