::: 科技專案

::: 研發成果

計畫名稱	智慧化駕駛輔助系統關鍵技術計畫
論文名稱	Vision-Based Crowd Pedestrian Detection
論文類型	國際研討會
發表處	IEEE International Conference on Digital Signal Processing, Singapore, 2015
發表人	Shih-Shinh Huang、Feng-Chia Chang、You-Chen Liu、Pei-Yung Hsiao
發表日期	2015/04/21
國家	國外
內容摘要	This paper proposes a crowd pedestrian detection based on monocular vision. To handle with the challenges faced in crowded scenes, such as occlusion, this study combines multiple cues to detect individuals in the observed image. Based on the assumptions that the human head is generally visible and background scene is stationary, all circular regions in the segmented foreground mask are firstly extracted by an algorithm called circle Hough transform (CHT). Each circle is then considered as the head candidate and further verified whether it is exactly an individual or a false one by combining multiple cues. Matching a candidate to a several constructed pedestrian templates is firstly applied for verification. Then, two proposed cues called head foreground contrast (HFC) and block color relation (BCR) are incorporated for further verification. In the experiment, three videos are used to validate the proposed method and the results show that the proposed one lowers the false positives at the expense of little detection rate.