Crowd Abnormal Behaviour Identification Based on Integral Optical Flow in Video Surveillance Systems

Informatica ◽  
2018 ◽  
Vol 29 (2) ◽  
pp. 211-232 ◽  
Author(s):  
Huafeng Chen ◽  
Olga Nedzvedz ◽  
Shiping Ye ◽  
Sergey Ablameyko
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Surveillance Systems ◽  
Abnormal Behaviour ◽  
Integral Optical Flow

scholarly journals Traffic extreme situations detection in video sequences based on integral optical flow

2019 ◽  
Vol 43 (4) ◽  
pp. 647-652 ◽  
Author(s):  
H. Chen ◽  
S. Ye ◽  
A. Nedzvedz ◽  
O. Nedzvedz ◽  
H. Lv ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Real World ◽  
Road Traffic ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Directional Movement ◽  
Background Motion ◽  
Random Background ◽  
Integral Optical Flow

Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.

scholarly journals Affective State Based Anomaly Detection in Crowd

2019 ◽  
Vol 24 (2) ◽  
pp. 134-140
Author(s):  
Glorija Baliniskite ◽  
Egons Lavendelis ◽  
Mara Pudane
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Positive Emotions ◽  
Emotional State ◽  
Affective State ◽  
Surveillance Systems ◽  
Abnormal Behaviour ◽  
Density Space

Abstract To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

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