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Privacy Preserving Crowd Monitoring:
Counting People without People Models or Tracking

There is currently a great interest in vision technology for monitoring all types of environments. This could have many goals, e.g. security, resource management, or advertising. Yet, the deployment of vision technology is invariably met with skepticism by society at large, given the perception that it could be used to infringe on the individuals' privacy rights. This tension is common in all areas of data-mining, but becomes an especially acute problem for computer vision for two reasons: 1) the perception of compromised privacy is particularly strong for technology which, by default, keeps a visual record of people's actions; 2) the current approaches to vision-based monitoring are usually based on object tracking or image primitives, such as object silhouettes or blobs, which imply some attempt to "identify" or "single out" the individual.

From the laymen's point of view, there are many problems in environment monitoring that can be solved without explicit tracking of individuals. These are problems where all the information required to perform the task can be gathered by analyzing the environment holistically: e.g. monitoring of traffic flows, detection of disturbances in public spaces, detection of speeding on highways, or estimation of the size of moving crowds. By definition, these tasks are based on either properties of 1) the "crowd" as a whole, or 2) an individual's "deviation" from the crowd. In both cases, to accomplish the task it should suffice to build good models for the patterns of crowd behavior. Events could then be detected as variations in these patterns, and abnormal individual actions could be detected as outliers with respect to the crowd behavior. This would preserve the individual's identity until there is good reason to do otherwise.

In this work, we introduce a new formulation for surveillance technology, which is averse to individual tracking and, consequently, privacy preserving. We illustrate this new formulation with the problem of pedestrian counting. This is a canonical example of a problem that vision technology addresses with privacy invasive methods: detect the people in the scene, track them over time, and count the number of tracks. Unlike these methods, we show that there is in fact no need for pedestrian detection, object tracking, or object-based image primitives to accomplish the pedestrian counting goal, even when the crowd is sizable and inhomogeneous, e.g. has sub-components with different dynamics. In fact, we argue that, when considered under the constraints of privacy-preserving monitoring, the problem actually appears to become simpler. We simply develop methods for segmenting the crowd into the sub-parts of interest (e.g. groups of people moving in different directions) and estimate the number of people by analyzing holistic properties of each component. This is shown to be quite robust and accurate. The system is also privacy-preserving in the sense that it can be implemented with hardware that does not produce a visual record of the people in the scene, i.e. with special-purpose cameras that output low-level features (e.g. segmentations, edges, and texture).

Selected Publications:
  • Analysis of Crowded Scenes using Holistic Properties
    A. B. Chan, M. Morrow, and N. Vasconcelos
    In 11th IEEE Intl. Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2009),
    Miami, June 2009.
    ? IEEE [pdf]
     
  • Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking
    A. B. Chan, Z. S. J. Liang, and N. Vasconcelos,
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Anchorage, June 2008.
    IEEE, [ps][pdf].
     
  • Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
    A. B. Chan and N. Vasconcelos,
    IEEE Transactions on Pattern Analysis and Machine Intelligence,
    Vol. 30(5), pp. 909-926, May 2008.
    IEEE, [ps][pdf].
Demos/
Results:
Databases:
  • Pedestrian Traffic Database [zip 755MB | readme]  
  • CVPR annotations and counting data [zip 2.6MB]  
  • PETS 2009 dataset [website]
Contact: Antoni Chan, Nuno Vasconcelos





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