A Hybrid Detection and Classification System for Human Motion Analysis
Ken Tabb, Neil Davey, Rod Adams, Stella George · 2002
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 ‘nonhuman’. The axis crossover vector method is used for translating the active contour into a scale-. location-, resolution- and 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.