Tracking people in video camera images using neural networks

Abstract

People are difficult targets to process in video surveillance and monitoring (VSAM) because of small size and non-rigid motion. In this paper, we address neural network application to people tracking for VSAM. A feedforward multilayer perceptron network (FMPN) is employed for the tracking in low-resolution image sequences using position, shape, and color cues. When multiple people are partly occluded by themselves, the foreground image patch of the people group detected is divided into individuals using another FMPN. This network incorporates three different techniques relying on a line connecting top pixels of the binary foreground image, the vertical projection of the binary foreground image, and pixel value variances of divided regions. The use of neural networks provides efficient tracking in real outdoor situations particularly where the detailed visual information of people is unavailable due mainly to low image resolution. © Springer-Verlag Berlin Heidelberg 2005.