PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Evaluating Multi-Object Tracking
Kevin Smith, Daniel Gatica-Perez, Jean-Marc Odobez and Sileye Ba
In: IEEE Conf. on Computer Vision and Pattern Recognition, Workshop on Empirical Evaluation Methods in Computer Vision (CVPR-EEMCV), June 2005, San Diego.

Abstract

Multiple object tracking (MOT) is an active and challenging research topic. Many different approaches to the MOT problem exist, yet there is little agreement amongst the community on how to evaluate or compare these methods, and the amount of literature addressing this problem is limited. The goal of this paper is to address this issue by providing a comprehensive approach to the empirical evaluation of tracking performance. To that end, we explore the tracking characteristics important to measure in a real-life application, focusing on configuration (the number and location of objects in a scene) and identification (the consistent labeling of objects over time), and define a set of measures and a protocol to objectively evaluate these characteristics.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1286
Deposited By:Jean-Marc Odobez
Deposited On:22 November 2006