TY - GEN
T1 - Video-based detection of abnormal activities in crowd using a combination of motion-based features
AU - Solmaz, Berkan
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Visual surveillance is of utmost importance for ensuring public safety and, detecting and preventing violent activities. The rapidly increasing number of surveillance cameras makes automated visual surveillance necessary, since monitoring a large of number of cameras by operators is not feasible, requiring a huge workload. In this paper, we propose a compact method for automated analysis of behaviours in crowds, specifically detecting the abnormal activities in crowd videos, which is one of the most critical applications of visual surveillance. The most intuitive way of abnormal activity detection is to consider common and typical activities in the scenes as normal and any unseen strange activities as anomalies that might be due to dangerous events. When tragic incidents such as accidents, disasters, shootings and violent behaviours happen, people tend to move in a very fast pace and in arbitrary directions. Thus, the proposed method consists of modelling the activities of crowds in the scenes during regular events, and analysing the spatial and temporal changes in their motion, which may be related to abnormal activities. For defining the crowd activities, first, crowd specific motion representations are computed. The computed representations utilize motion attributes such as speed, direction and acceleration of people in the crowds. Next, by employing these representations, typical activities in crowd videos, related to normal behaviours of people when no abnormal activities are present, are learned. Later, the distributions of motion representations are inspected; abrupt changes in the distributions of motion representations, occurring in several parts of the scenes, are labelled as anomalies. Experiments, conducted on a publicly available dataset, involving videos of crowds, reveal that the proposed method is effective in detecting abnormal activities. Additionally, quantitative performance of variants of the proposed method including the baseline approaches were measured for a comparison.
AB - Visual surveillance is of utmost importance for ensuring public safety and, detecting and preventing violent activities. The rapidly increasing number of surveillance cameras makes automated visual surveillance necessary, since monitoring a large of number of cameras by operators is not feasible, requiring a huge workload. In this paper, we propose a compact method for automated analysis of behaviours in crowds, specifically detecting the abnormal activities in crowd videos, which is one of the most critical applications of visual surveillance. The most intuitive way of abnormal activity detection is to consider common and typical activities in the scenes as normal and any unseen strange activities as anomalies that might be due to dangerous events. When tragic incidents such as accidents, disasters, shootings and violent behaviours happen, people tend to move in a very fast pace and in arbitrary directions. Thus, the proposed method consists of modelling the activities of crowds in the scenes during regular events, and analysing the spatial and temporal changes in their motion, which may be related to abnormal activities. For defining the crowd activities, first, crowd specific motion representations are computed. The computed representations utilize motion attributes such as speed, direction and acceleration of people in the crowds. Next, by employing these representations, typical activities in crowd videos, related to normal behaviours of people when no abnormal activities are present, are learned. Later, the distributions of motion representations are inspected; abrupt changes in the distributions of motion representations, occurring in several parts of the scenes, are labelled as anomalies. Experiments, conducted on a publicly available dataset, involving videos of crowds, reveal that the proposed method is effective in detecting abnormal activities. Additionally, quantitative performance of variants of the proposed method including the baseline approaches were measured for a comparison.
KW - Abnormal behaviour detection
KW - Anomaly detection
KW - Crowd activity.
KW - Video analysis
UR - http://www.scopus.com/inward/record.url?scp=85057432333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057432333&partnerID=8YFLogxK
U2 - 10.1117/12.2325527
DO - 10.1117/12.2325527
M3 - Conference contribution
AN - SCOPUS:85057432333
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II
A2 - Bouma, Henri
A2 - Prabhu, Radhakrishna
A2 - Stokes, Robert James
A2 - Yitzhaky, Yitzhak
PB - SPIE
T2 - Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II 2018
Y2 - 10 September 2018 through 11 September 2018
ER -