Detecting motion patterns via direction maps with application to surveillance

Detection of motion patterns in video data can be significantly simplified by abstracting away from pixel intensity values towards representations that explicitly and compactly capture movement across space and time. A novel representation that captures the spatiotemporal distributions of motion across regions of interest, called the ''Direction Map,'' abstracts video data by assigning a two-dimensional vector, representative of local direction of motion, to quantized regions in space-time. Methods are presented for recovering direction maps from video, constructing direction map templates (defining target motion patterns of interest) and comparing templates to newly acquired video (for pattern detection and localization). These methods have been successfully implemented and tested (with real-time considerations) on over 6300 frames across seven surveillance/traffic videos, detecting potential targets of interest as they traverse the scene in specific ways. Results show an overall recognition rate of approximately 91% hits vs 8% false positives.