TY - GEN
T1 - Vision-based crowd pedestrian detection
AU - Huang, Shih Shinh
AU - Chang, Feng Chia
AU - Liu, You Chen
AU - Hsiao, Pei Yung
AU - Ho, Hong Fa
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - 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.
AB - 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.
KW - block color relation
KW - circular Hough transform
KW - crowd pedestrian detection
KW - head foreground contrast
UR - http://www.scopus.com/inward/record.url?scp=84961384874&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961384874&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2015.7252002
DO - 10.1109/ICDSP.2015.7252002
M3 - Conference contribution
AN - SCOPUS:84961384874
T3 - International Conference on Digital Signal Processing, DSP
SP - 878
EP - 881
BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Digital Signal Processing, DSP 2015
Y2 - 21 July 2015 through 24 July 2015
ER -