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Active tracking and pursuit under different levels of occlusion: a two-layer approach

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Abstract

We present an algorithm for real-time, robust, vision-based active tracking and pursuit. The algorithm was designed to overcome problems arising from active vision-based pursuit, such as target occlusion. Our method employs two layers to deal with occlusions of different lengths. The first layer is for short- or medium-term occlusions: those where a known method—such as mean shift combined with a Kalman filter—fails. For this layer we designed the hybrid filter for active pursuit (HAP). HAP utilizes a Kalman filter modified to respond to two different modes of action: one in which the target is positively identified and one in which the target identification is uncertain. For long-term occlusions we use the second layer. This layer is a decision algorithm that follows a learning procedure and is based on game theory-related reinforcement (Cesa-Bianchi and Lugosi, Prediction Learning and Games, 2006). The learning process is based on trial and error and is designed to perform adequately with a small number of samples. The algorithm produces a data structure that can be shared among agents or sent to a central control of a multi-agent system. The learning process is designed so that agents perform tasks according to their skills: an efficient agent will pursue targets while an inefficient agent will search for entering targets. These capacities make this system well suited for embedding in a multi-agent control system.

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Acknowledgments

The authors deeply thank Orly and Daniel Ghosalker for their contribution to the machine learning section, Daniel Sigalov for his constructive remarks, and Amir Geva for the programming of NEMALA. Gadi Katzir was supported during the writing by an ISF grant. Tomer Baum thanks Boreh Olam for everything.

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Authors and Affiliations

  1. Mathematics Department, Technion, Israel Institute of Technology, 32000 , Haifa, Israel

    Tomer Baum

  2. Computer Science Department, Technion, Israel Institute of Technology, 32000 , Haifa, Israel

    Idan Izhaki & Ehud Rivlin

  3. Department of Evolutionary and Environmental Biology, University of Haifa, 31905 , Mount Carmel, Israel

    Gadi Katzir

  4. Department of Marine Biology, University of Haifa, 31905 , Mount Carmel, Israel

    Gadi Katzir

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  1. Tomer Baum
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  2. Idan Izhaki
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  3. Ehud Rivlin
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  4. Gadi Katzir
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Correspondence to Tomer Baum.

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Baum, T., Izhaki, I., Rivlin, E. et al. Active tracking and pursuit under different levels of occlusion: a two-layer approach. Machine Vision and Applications 25, 173–184 (2014). https://doi.org/10.1007/s00138-013-0520-2

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  • DOI: https://doi.org/10.1007/s00138-013-0520-2

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