A Hybrid Detection and Classfication System for Human Motion Analysis

This paper discusses a hybrid technique for detecting and tracking moving pedestrians in a video sequence. The technique comprises two sub-systems: an active contour model for detecting and tracking moving objects in the visual field, and an MLP neural network for classifying the moving objects being tracked as human or non-human . The axis crossover vector method is used for translating the active contour into a scale-. location-, resolutionand rotation-invariant vector suited for input to a neural network, and we identify the most appropriate level of detail for encoding human shape information. Experiments measuring the neural network s accuracy at classifying unseen computer generated and real moving objects are presented, along with potential applications of the technology. Previous work has accommodated lateral pedestrian movement across the visual field; this paper describes a system which accommodates arbitrary angles of pedestrian movement on the ground plane.