Object Reacquisition and Tracking in Large-Scale Smart Camera Networks

Proceedings of the First ACM/IEEE International Conference on Distributed Smart Cameras, Vienna

Object reacquisition or reidentification is the process of matching
objects between images taken from separate cameras. In
this paper, we present our work on feature based object reidentification
performed on autonomous embedded smart cameras
and applied to traffic scenarios. We present a novel approach
based on PCA-SIFT features and a vocabulary tree.
By building unique object signatures from visual features,
reidentification can be done efficiently coevally minimizing
the communication overhead between separate camera nodes.
Applied to large-scale traffic scenarios, important parameters
including travel time, travel time variability, section density,
and partial dynamic origin/destination demands can be obtained.
The proposed approach works on spatially separated, uncalibrated,
non-overlapping cameras, is highly scalable and
solely based on appearance-based optical features. The entire
system is implemented and evaluated with regard to a typical
embedded smart camera platform featuring one single Texas
Instruments TM fixed-point DSP.

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