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Multi-object tracking evaluated on sparse events

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Abstract

This article presents a visual object tracking method and applies an event-based performance evaluation metric for assessment. The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments. The tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, a performance evaluation method is presented and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These events are extracted automatically from the different trackers an their varying types of low level tracking results. Then, a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of multiple public datasets.

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Acknowledgements

The authors gratefully acknowledge support by the Swiss SNF NCCR project IM2 and EU project HERMES (FP6-027110). Furthermore, we would like to thank Prof. Dr. Thomas Moeslund from the University of Aalborg, Denmark and Dr. Jordi Gonzalez from the CVC Center in Barcelona, Spain for their tracking results and valuable input.

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Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, CH-8092, Zurich, Switzerland

    Daniel Roth, Esther Koller-Meier & Luc Van Gool

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  1. Daniel Roth
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  2. Esther Koller-Meier
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  3. Luc Van Gool
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Correspondence to Daniel Roth.

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Roth, D., Koller-Meier, E. & Van Gool, L. Multi-object tracking evaluated on sparse events. Multimed Tools Appl 50, 29–47 (2010). https://doi.org/10.1007/s11042-009-0365-x

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