Abstract
Main challenges of pedestrian detection are caused by the intra-class variation of pedestrians in clothing, scales, deformations, occlusions, and backgrounds. The prevalent detection frameworks employ a series of dense sliding windows, which are time-consuming. In this work, we equip the detection framework with another new strategy, and extract the new features, to eliminate the above requirements. Segmentation by weighted aggregation (SWA) provides a probability measure to segment objects from complex backgrounds. Perceptual hash (pHash) has shown its power in similar image retrieval because it is modification-tolerant and scale-invariant. The proposed approach uses binarized normed gradients (BING) to efficiently generate a small set of estimation proposals, and formulates SWA and pHash into a joint descriptor, called HASP, to improve the detection performance significantly. Experimental results both on INRIA dataset and ETH dataset have demonstrated the effectiveness and efficiency of the proposed approach.
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