Multi-orientation saliency features fusion based multi-object detection
Hong Lu, Hao Tang, Shumin Fei, Weifeng Cao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Robustly and automatically detecting multi-object is an important and challenging task in complex and dynamic scenarios where the object features, illumination and background etc., are often time-variable. In this paper, a novel frame work of multi-object detection is presented based on multi-orientation saliency features fusion. Firstly, four orientations Gabor filtering is used to extract the saliency features from image sequence. Then, grayscale morphological processing, area filtering and binarization are employed to highlight the possible object regions. Furthermore, the duty ratio and scale ratio of every possible region are utilized to select the candidate object regions. Finally, the intersection states among four orientations candidate regions are judged, and the optimal object region is obtained by weighted fusing in terms of intersection area and candidate region duty ratios. Results from experiments show the excellent performance of the proposed algorithm in unrestrained and complex scenarios.