关键词: 多目标检测, 多方向显著子区域, 交集状态判断, 交集面积占空比, 置信表决

Abstract: Multi-object detection under the smart traffic surveillance system is an important and challenging task. This paper presents a multi-object detection algorithm based on multi-orientation saliency subregion voting by confidence. Four-orientation saliency feature pixels are firstly extracted by Gabor filters. The valid subregions and scales of each object are further confirmed in terms of the multi-threshold criteria. And then valid intersection subregions and intersection states are confirmed. The duty ratios of intersection area and the relative duty ratios of valid intersection subregions are weighted and integrated to acquire the confidence vote coefficient for every subregion. The optimal detection is finally derived according to the maximum confidence vote. Experiments conducted on real traffic image sequences demonstrate that the proposed method is robust and accurate in multi-object detection under mutual disturbance and occlusion among adjacent objects, object shadow, non-uniform illumination and similar color disturbance in the background etc.

Key words: multi-object detection, multi-orientation saliency subregion, intersection state judging, duty ratios of intersection area, voting by confidence