Probabilistic color matching and tracking of human subjects

Abstract

Pattern discovery algorithms based on the computational mechanics (CM) method have been shown to succinctly describe underlying patterns in data through the reconstruction of minimum probabilistic finite state automata (PFSA). We apply the CM approach toward the tracking of human subjects in real time by matching and tracking the underlying color pattern as observed from a fixed camera. Objects are extracted from a video sequence, and then raster scanned, decomposed with a one-dimensional Haar wavelet transform, and symbolized with the aid of a red-green-blue (RGB) color cube. The clustered causal state algorithm is then used to reconstruct the corresponding PFSA. Tracking is accomplished by generating the minimum PFSA for each subsequent frame, followed by matching the PFSAs to the previous frame. Results show that there is an optimum alphabet size and segmentation of the RGB color cube for efficient tracking.

Contour coding based rotating adaptive model for human detection and tracking in thermal catadioptric omnidirectional vision

“…However, most surveillance systems [1][2][3][4] rely on the conventional imaging system that requires proper illumination and has a limited field of view. In this paper, we propose a novel surveillance system for human detection and tracking with thermal catadioptric omnidirectional (TCO) vision, which consists of a thermal camera and a catadioptric omnidirectional sensor.…”