TY - GEN
T1 - Extracting Spatio-Temporal Texture signatures for crowd abnormality detection
AU - Hao, Yu
AU - Wang, Jing
AU - Liu, Ying
AU - Xu, Zhijie
AU - Fan, Jiulun
N1 - Publisher Copyright:
© 2017 Chinese Automation and Computing Society in the UK - CACSUK.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices - called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications.
AB - In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices - called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications.
KW - Crowd abnormality
KW - Spatio-Temporal Volume
KW - STT Signature
UR - http://www.scopus.com/inward/record.url?scp=85039999711&partnerID=8YFLogxK
U2 - 10.23919/IConAC.2017.8082051
DO - 10.23919/IConAC.2017.8082051
M3 - Conference Proceeding
AN - SCOPUS:85039999711
T3 - ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing
BT - ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
A2 - Zhang, Jie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Automation and Computing, ICAC 2017
Y2 - 7 September 2017 through 8 September 2017
ER -