A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Performance Evaluation of People Tracking Systems
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Tools and Techniques for Video Performance Evaluation
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
Performance Evaluation Metrics for Motion Detection and Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
ACM Computing Surveys (CSUR)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
Partition-distance methods for assessing spatial segmentations of images and videos
Computer Vision and Image Understanding
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Event-Based Tracking Evaluation Metric
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Dynamic Performance Measures for Object Tracking Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Video Surveillance Online Repository (ViSOR): an integrated framework
Multimedia Tools and Applications
Multimedia Tools and Applications
Performance evaluation of object detection algorithms for video surveillance
IEEE Transactions on Multimedia
Objective evaluation of video segmentation quality
IEEE Transactions on Image Processing
Performance measures for video object segmentation and tracking
IEEE Transactions on Image Processing
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
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Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms.