Audio-visual tracking for natural interactivity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the conference on Visualization '01
Real-Time Tracking for Enhanced Tennis Broadcasts
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Logic-based trajectory evaluation in videos
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Multiple-Person tracking using a plan-view map with error estimation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
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A tracking system outputs a separate motion trajectory for each moving object in a scene. This paper presents a problem of performance evaluation and performance metrics for real-time systems that track people, or moving objects, in video sequences, and it proposes performance measurement methodology for such systems. Two approaches to measuring performance are presented. The first approach compares the computed motion trajectories to the reference trajectories. It enables a complete evaluation of tracking results, but reference trajectories it requires are difficult to get. The second, more practical approach identifies in the computed trajectories specific discrete events, such as line crossings, and compares sequences of these events to sequences of reference events, which are much easier to obtain than reference trajectories. These events can usually be chosen such that they reflect the application goal of a tracking system, e.g. counting people in an area. Precision of evaluation increases with density of events. Short event sequences measure the sensitivity and selectivity of a tracking method, i.e. how well it satisfies the ``one person -- one trajectory" objective. Long sequences measure continuity of trajectories: how long a method can keep track of one person. The paper shows performance measurement results for a real-time people tracking system developed by the authors.