Abstract

T. N. Mundhenk, J. Everist, C. Landauer, L. Itti, K. Bellman, Distributed biologically-based real-time tracking in the absence of prior target information, In: Proc. SPIE International Conference on Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, (D. P. Casasent, E. L. Hall, J. Roning Ed.), Vol. 6006, pp. 142-153, Bellingham, WA:SPIE Press, Oct 2005. (Cited by 10)

Abstract: We are developing a distributed system for the tracking of people and objects in complex scenes and environments using biologically based algorithms. An important component of such a system is its ability to track targets from multiple cameras at multiple viewpoints. As such, our system must be able to extract and analyze the features of targets in a manner that is sufficiently invariant of viewpoints, so that they can share information about targets, for purposes such as tracking. Since biological organisms are able to describe targets to one another from very different visual perspectives, by discovering the mechanisms by which they understand objects, it is hoped such abilities can be imparted on a system of distributed agents with many camera viewpoints. Our current methodology draws from work on saliency and center surround competition among visual components that allows for real time location of targets without the need for prior information about the targets visual features. For instance, gestalt principles of color opponencies, continuity and motion form a basis to locate targets in a logical manner. From this, targets can be located and tracked relatively reliably for short periods. Features can then be extracted from salient targets allowing for a signature to be stored which describes the basic visual features of a target. This signature can then be used to share target information with other cameras, at other viewpoints, or may be used to create the prior information needed for other types of trackers. Here we discuss such a system, which, without the need for prior target feature information, extracts salient features from a scene, binds them and uses the bound features as a set for understanding trackable objects.

Themes: Computational Modeling, Computer Vision