TY - GEN
T1 - Moving cast shadow detection in video based on new chromatic criteria and statistical modeling
AU - Shi, Hang
AU - Liu, Chengjun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - A novel moving cast shadow detection method is presented in this paper to detect and remove the cast shadows from the foreground. First, the foreground is detected using the global foreground modeling (GFM) method. Second, the moving cast shadow is detected and removed from the foreground using a new moving cast shadow detection method that contains four hierarchical steps. In the first step, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. In the second step, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. In the third step, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. In the last step, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data 'Highway-3' and the real traffic data from the New Jersey Department of Transportation (NJDOT) show the feasibility of the proposed method.
AB - A novel moving cast shadow detection method is presented in this paper to detect and remove the cast shadows from the foreground. First, the foreground is detected using the global foreground modeling (GFM) method. Second, the moving cast shadow is detected and removed from the foreground using a new moving cast shadow detection method that contains four hierarchical steps. In the first step, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. In the second step, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. In the third step, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. In the last step, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data 'Highway-3' and the real traffic data from the New Jersey Department of Transportation (NJDOT) show the feasibility of the proposed method.
KW - New chromatic criteria
KW - Shadow detection
KW - Shadow region detection
KW - Statistical shadow modeling
UR - http://www.scopus.com/inward/record.url?scp=85080854858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080854858&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2019.00038
DO - 10.1109/ICMLA.2019.00038
M3 - Conference contribution
AN - SCOPUS:85080854858
T3 - Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
SP - 196
EP - 201
BT - Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
A2 - Wani, M. Arif
A2 - Khoshgoftaar, Taghi M.
A2 - Wang, Dingding
A2 - Wang, Huanjing
A2 - Seliya, Naeem
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Y2 - 16 December 2019 through 19 December 2019
ER -