Tracking and activity recognition through consensus in distributed camera networks

  • Authors:

  • Bi Song;Ahmed T. Kamal;Cristian Soto;Chong Ding;Jay A. Farrell;Amit K. Roy-Chowdhury

  • Affiliations:

  • University of California, Riverside, CA;University of California, Riverside, CA;Digital Western, San Jose, CA and University of California, Riverside, CA;University of California, Riverside, CA;University of California, Riverside, CA;University of California, Riverside, CA

  • Venue:
  • IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
  • Year:
  • 2010

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Abstract

Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems--tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.