Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems

Chen, H.; Ye, Sh.; Nedzvedz, O.; Ablameyko, Sergey

doi:10.1007/s10812-018-0622-8

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…A motion map is an image where a feature Q at each position shows information about movement of pixels whose motion path relate to this position or a region centered at this position. We defined several motion maps [20], which describe comprehensive information of pixel motion, including direction, intensity, symmetry / directionality, etc. This information reflects behaviour of objects, in this case, moving cars on the road, thus certain events can be detected.…”

Section: Integral Optical Flow and Image Motion Mapsmentioning

confidence: 99%

“…It computed interaction forces between particles based on their velocities. Chen et al [19,20] proposed to use integral optical flow for dynamic object monitoring and in particularly for crowd behavior identification. As we showed there, integral optical flow allows one to identify complex dynamic object behaviors based on the interactions between pixels without the need to track objects individually.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Nedzved

et al. 2019

Computer Optics

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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.

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Section: Integral Optical Flow and Image Motion Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Nedzved

et al. 2019

Computer Optics

Self Cite

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show abstract

“…Consequently, putting in place real-time, accurate fighting behaviour recognition to aid security agents and ensure their protection is crucial. Furthermore, optical flow information derived from video sequence pixel shifts has strong spatial and temporal properties, and it is commonly used in video processing to explain target motion patterns [8]. Thus, in recent years, optical flow-based battle detection has gotten much coverage.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based System for the Detection of Human Violence in Video Data

Shoaib¹,

Sayed²

2021

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The number of security cameras positioned within the surrounding area has expanded, increasing the demand for automatic activity recognition systems. In addition to offline assessment and the issuance of an ongoing alarm in the case of aberrant behaviour, automatic activity detection systems can be employed in conjunction with human operators. In the proposed research framework, an ensemble of Mask Region-based Convolutional Neural Networks for key-point detection scheme, and LSTM based Recurrent Neural Network is used to create a deep neural network model (Mask RCNN) for recognizing violent activities (i.e. kicking, punching, etc.) of a single person. First of all, the key-points locations and ground-truth masks of humans in an image are selected using the selected region; the temporal information is extracted. Experimental results show that the ensemble model outperforms individual models. The proposed technique has a reasonable accuracy rate of 77.4 percent, 95.7 percent, and 88.2 percent, respectively, on the Weizmann, KTH, and our custom datasets. As the proposed effort applies to industry and in terms of security, it is beneficial to society.

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“…Therefore, it is crucial to achieve the real-time and reliable fight behavior recognition to assist security agents and ensure safety. The optical flow information extracted from the changes in pixels of the video sequences has satisfactory spatial and temporal characteristics, which is widely used to describe the target's motion tendency in video processing [9]. In recent years, the fighting detection based on the optical flow has been widely concerned.…”

Section: Introductionmentioning

confidence: 99%

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

Chong

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

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Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems

Cited by 10 publications

References 4 publications

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

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

A Deep Learning Based System for the Detection of Human Violence in Video Data

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

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