A Comparison of a Chaos-Theoretic Method for Pre-Attentive Vision with Traditional Grayscale-Based Methods

Accurate and robust attention direction has been of substantial interest in the computer vision community, particularly for industrial surveillance systems that initiate recording at the onset of motion or an interesting contextual event. One key issue is minimizing false alarms to limit video record bandwidth and capacity. One issue that these systems face is high false alarm rates under sudden illumination change. In this paper we propose a system which applies measures from chaos theory and fractal analysis to provide a robust pre-attentive processing engine for motion detection. Results compare quite favorably in terms of probability of detection versus false detection rate against traditional methods for low-level change detection, namely Sum of Absolute Differences, and Gaussian Mixture Models. The proposed chaos-based method is shown to have superior performance. Additionally the proposed approach has an intuitive justification based on creation and flow of information between image frames, and consequently a very intuitive and problem-based threshold determination.