Abstract: Pedestrian detection has become a hot topic in the field of computer vision. Non-maximal suppression combined with hard threshold is the most common post-process method in pedestrian detection, whereas it is easy to cause false positive and false negative. As to this problem, this paper presents a pedestrian-object detection method based on similarity measurement. Firstly, Faster RCNN is used to build a series of candidate proposals among which initial selection is made based on non-maximal suppression. Then the authors create feature templates by target areas with high confidence, and make a further selection in the low-confidence proposals according to the feature similarity. Lastly, the detection results are composed of the reserved proposals and the templates. The experimental results from VOC, INRIA, Caltech datasets demonstrate that similarity measurement method can achieve higher pedestrian detection performance.

Key words: pedestrian detection, object proposals selecting, feature similarity measurement, template matching