Incremental Recognition Of Pedestrians From Image Sequences

Abstract

An approach that uses a volume model consisting of cylinders for model-based recognition of pedestrians in real-world images is presented. The human body is represented by a volume model, and medical motion data are used for simulating the movement of walking. This knowledge is exploited to determine the 3-D position, as well as the posture of an observed person. By applying a Kalman filter, the model parameters in consecutive images are incrementally estimated. The approach is tested on real image data.<>