Effective object proposals: size prediction for pedestrian detection in surveillance videos
Ji Wei Qiu, Lide Wang, Yu Hen Hu, Yin Wang · Electronics Letters · 2020
Though detectors based on deep networks have witnessed a significant boost in pedestrian detection performance, small‐scale pedestrian detection remains to be a challenging task. To this end, the authors propose a scale prediction method to eliminate the dependence of most existing detectors on pre‐defined anchor boxes. Due to projective transformation, a pedestrian standing afar will appear smaller than one standing closeby. For a stationary surveillance camera, different blocks on the image correspond to the views of various depths leadings to diverse sizes of object proposals. A neural network structure is developed to empirically estimate the size of a pedestrian candidate in the pixel coordinates given its central location. Comprehensive sets of experiments on two real‐world datasets demonstrate that the proposed method achieves superior performance with state‐of‐the‐art methods. Besides, significant performance advantages on small pedestrian detection are observed.