TY - GEN
T1 - Detection of groups of people in surveillance videos based on spatio-temporal clues
AU - Mora-Colque, Rensso V.H.
AU - Cámara-Chávez, Guillermo
AU - Schwartz, William Robson
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Video surveillance has been widely employed in our society in the past years. In this context, humans play an important role and are the major players since they are responsible for changing the state of the scene through actions and activities. Therefore, the design of automatic methods to understand human behavior and recognize activities are important to determine which subjects are involved in an activity of interest. The computer vision research area has contributed vastly for the development of methods related to detection, tracking and recognition of humans. However, there is still a lack of methods able to recognize higher level activities (e.g., interaction among people that might be involved in an illegal activity). The first step to be successful in this enterprise is to detect and locate groups of people in the scene, which is essential to make inferences regarding interactions among persons. Aiming at such direction, this paper presents a group detection approach that combines motion and spatial information with low-level descriptors to be robust to situations such as partial occlusions. The experimental results obtained using the PETS 2009 and the BEHAVE datasets demonstrate that the proposed combination indeed achieves higher accuracies, indicating a promising direction for future research.
AB - Video surveillance has been widely employed in our society in the past years. In this context, humans play an important role and are the major players since they are responsible for changing the state of the scene through actions and activities. Therefore, the design of automatic methods to understand human behavior and recognize activities are important to determine which subjects are involved in an activity of interest. The computer vision research area has contributed vastly for the development of methods related to detection, tracking and recognition of humans. However, there is still a lack of methods able to recognize higher level activities (e.g., interaction among people that might be involved in an illegal activity). The first step to be successful in this enterprise is to detect and locate groups of people in the scene, which is essential to make inferences regarding interactions among persons. Aiming at such direction, this paper presents a group detection approach that combines motion and spatial information with low-level descriptors to be robust to situations such as partial occlusions. The experimental results obtained using the PETS 2009 and the BEHAVE datasets demonstrate that the proposed combination indeed achieves higher accuracies, indicating a promising direction for future research.
KW - Collective behavior
KW - Group activity
KW - Group detection
KW - Low-level descriptors
UR - https://www.scopus.com/pages/publications/84949154478
U2 - 10.1007/978-3-319-12568-8_115
DO - 10.1007/978-3-319-12568-8_115
M3 - Conference contribution
AN - SCOPUS:84949154478
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 948
EP - 955
BT - Progress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
A2 - Bayro-Corrochano, Eduardo
A2 - Hancock, Edwin
PB - Springer Verlag
T2 - 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
Y2 - 2 November 2014 through 5 November 2014
ER -