Low-complexity scalable distributed multicamera tracking of humans

  • Authors:

  • Sebastian Gruenwedel;Vedran Jelaca;Jorge Oswaldo Nino-Castaneda;Peter van Hese;Dimitri van Cauwelaert;Dirk van Haerenborgh;Peter Veelaert;Wilfried Philips

  • Affiliations:

  • Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium;Ghent University TELIN-IPI-IBBT, Gent, Belgium

  • Venue:
  • ACM Transactions on Sensor Networks (TOSN)
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

Real-time tracking of people has many applications in computer vision, especially in the domain of surveillance. Typically, a network of cameras is used to solve this task. However, real-time tracking remains challenging due to frequent occlusions and environmental changes. Besides, multicamera applications often require a trade-off between accuracy and communication load within a camera network. In this article, we present a real-time distributed multicamera tracking system for the analysis of people in a meeting room. One contribution of the article is that we provide a scalable solution using smart cameras. The system is scalable because it requires a very small communication bandwidth and only light-weight processing on a “fusion center” which produces final tracking results. The fusion center can thus be cheap and can be duplicated to increase reliability. In the proposed decentralized system all low level video processing is performed on smart cameras. The smart cameras transmit a compact high-level description of moving people to the fusion center, which fuses this data using a Bayesian approach. A second contribution in our system is that the camera-based processing takes feedback from the fusion center about the most recent locations and motion states of tracked people into account. Based on this feedback and background subtraction results, the smart cameras generate a best hypothesis for each person. We evaluate the performance (in terms of precision and accuracy) of the tracker in indoor and meeting scenarios where individuals are often occluded by other people and/or furniture. Experimental results are presented based on the tracking of up to 4 people in a meeting room of 9 m by 5 m using 6 cameras. In about two hours of data, our method has only 0.3 losses per minute and can typically measure the position with an accuracy of 21 cm. We compare our approach to state-of-the-art methods and show that our system performs at least as good as other methods. However, our system is capable to run in real-time and therefore produces instantaneous results.