Segmentation by weighted aggregation and perceptual hash for pedestrian detection

•A joint BING–HASP framework for pedestrian detection is proposed.•Segmentation by weighted aggregation is used to segment the salient regions from complex backgrounds.•Perception hash coded from the segmented image is suitable for pedestrian detection.•BING can significantly accelerate the computation process.

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.