Pedestrian Recognition from a Moving Catadioptric Camera

Abstract

This paper presents a real-time system for vision-based pedestrian recognition from a moving vehicle-mounted catadioptric camera. For efficiency, a rectification of the catadioptric image using a virtual cylindrical camera is employed. We propose a novel hybrid combination of a boosted cascade of wavelet-based classifiers with a subsequent texture-based neural network involving adaptive local features as final cascade stage. Within this framework, both fast object detection and powerful object classification are combined to increase the robustness of the recognition system. Further, we compare the hybrid cascade framework to a state-of-the-art multi-cue pedestrian recognition system utilizing shape and texture cues. Image distortions of the objects of interest due to the virtual cylindrical camera transformation are both explicitly and implicitly addressed by shape transformations and machine learning techniques. In extensive experiments, both systems under consideration are evaluated on a real-world urban traffic dataset. Results show the contributions of the various components in isolation and document superior performance of the proposed hybrid cascade system.