Pedestrian Detection with Fusion of Multi-models and Intra-frame Information

Pedestrian detection has significant applications in the field of driver assistance, video surveillance, intelligent robot and so on. To address the problem of many false detections and missing windows produced by existing pedestrian detection algorithm when the videos are of low resolution with complicated background, this paper propose a fusion method with multi-models and intra-frame information for pedestrian detection. It combines complementary detection results generated from Fast R-CNN and Faster R-CNN which can obtain more precise detection windows at first. Then a frame-context information fusion method is proposed to further remove the false positives and false negatives resulting from single frame information. In the Caltech pedestrian detection dataset, under the condition of false positive per image(FPPI) equaling 10%, the missing rate can be reduced to 14.04% which is 2.05% lower than that(16.09%) of Faster R-CNN model. It shows that fusion of multi-models and intra-frame information can correct the results of previous pedestrian detection and improve the detection performance accordingly.