Component-based Pedestrian Detection with Monocular Moving Camera
Chen Pan-jun · Jisuanji fangzhen · 2006
In environments where a camera is mounted on a freely moving platform, e.g. a vehicle, pedestrian detection becomes much more difficult. Especially, in cluttered scenes, the pedestrian detection is more challenging. A coarse-to-fine method for pedestrian detection was proposed in such environments. An individual human was modeled as an assembly of natural body parts, including head-shoulder, torso, and leg. Absolute Haar-like features and Edgelet features [1] were introduced. Part detectors, based on these features, were learnt from training images by Soft Cascade [2]. Firstly, the pedestrian candidates were generated by full-body detector. Then Bayesian decision base combination approach was utilized to determine pedestrians among those pedestrian candidates and could reduce the false alarm rate significantly. Experimental results show the method has high performance in natural cluttered scenes.