Evaluation of Matching Metrics for Trajectory-Based Indexing and Retrieval of Video Clips

Authors:
Shehzad Khalid;Andrew Naftel
Affiliations:
University of Manchester, UK;University of Manchester, UK
Venue:
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Year:
2005

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Abstract

This paper describes a comparative evaluation of three different similarity metrics for trajectory-based indexing and retrieval of video motion clips. The motion paths are generated using a low-level tracking algorithm incorporating first-order Kalman filter and colour appearance models. For simple motion paths, a RANSAC approach can be used to generate smooth trajectories for each tracked object described by low-order polynomials. This allows us to obtain a representative trajectory model even in the case of high numbers of outlier points caused by target mis-detection and multiple occlusions. We show that more complex trajectories including stop-start motions, can be modelled as time series using high order Chebyshev polynomials. Similarity metrics based on coefficient descriptors are shown to have comparable performance to a Hausdorff distance measure when retrieving trajectory-based motion clips but at substantially reduced computational cost. Experimental results are presented to illustrate the comparative performance of different matching metrics on real-world trajectory data collected by a retail store CCTV installation.