Many existing human detection systems are based on sub-window classification, namely detection is done by enumerating rectangular sub-images in the 2D image space. Detection rate of such approaches may be affected by perspective distortion and tilted orientation of the human in images. To overcome this problem without re-training the classifier, we develop a 3D search method. A search grid is defined in the 3D scene. At each grid point a rectified sub-image is generated to approximate the orthogonal projection of the target, so that the distortion due to camera setting is reduced. In addition, 3D target position can be estimated from single camera data. Experiments on challenging data from the PETS2007 and CAVIAR INRIA datasets show significantly improved detection performance of our approach compared with the 2D search-based methods.
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