Global Anomaly Crowd Behavior Detection Using Crowd Behavior Feature Vector

Yin, Yong; Liu, Qiannan; Mao, Shibiao

doi:10.14257/ijsh.2015.9.12.16

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…where k refers to the number of independent symbols and f z is the frequency of the z-th pixel in the image [24,32]. b) Occupancy Measure (OM): This measure refers to the area occupied by the detected objects over time.…”

Section: Frame Difference Map (Fdm)mentioning

confidence: 99%

Online video-based abnormal detection using highly motion techniques and statistical measures

Al-Dhamari¹,

Sudirman²,

Mahmood³

et al. 2019

TELKOMNIKA

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At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time.

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Section: Frame Difference Map (Fdm)mentioning

confidence: 99%

Online video-based abnormal detection using highly motion techniques and statistical measures

Al-Dhamari¹,

Sudirman²,

Mahmood³

et al. 2019

TELKOMNIKA

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show abstract

“…Yin et al [100] used the parameter of population density to make full use of the crowd density characteristics and dynamic characteristics. It was proposed a novel method by increasing the dimension of feature vector to increase the information content in order to improve the recognition accuracy.…”

Section: Abnormal Crowd Behaviormentioning

confidence: 99%

Detection of violent events in video sequences based on the census transform histogram operator

Quispe¹

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Surveillance systems in video sequences have been widely used to monitor scenes in various environments, such as airports, banks, schools, industries, bus and train stations, highways and stores. Due to the large amount of information obtained via surveillance cameras, the use of visual inspection by camera operators becomes a task subject to fatigue and failure, in addition to consuming a lot of time. One challenge is the development of intelligent surveillance systems capable of analyzing long video sequences captured by a network of cameras in order to identify a certain behavior. In this work, we propose and analyze the use of several classification techniques, based on the CENTRIST (Transformation Census Histogram) operator, in the context of identifying violent events in video scenes. Additionally, we evaluated other traditional descriptors, such as HoG (Oriented Gradient Histogram), HOF (Optical Flow Histogram) and descriptors extracted from pre-trained deep machine learning models. In order to allow the evaluation only in regions of interest present in the video frames, we investigated techniques for removing the background from the scene. A sliding window-based approach was used to assess smaller regions of the scene in combination with a voting criterion. The sliding window is then applied along with block filtering using the optical flow of the scene. To demonstrate the effectiveness of our method for discriminating violence in crowd scenes, we compared the results to other approaches available in the literature in two public databases (Violence in Crowds and Hockey Fights). The combination of CENTRIST and HoG was demonstrated in comparison to the use of these operators individually. The combination of both operators obtained approximately 88% against 81% using only HoG and 86% using CENTRIST. From the refinement of the proposed method, we identified that evaluating blocks of the frame with the sliding window-based approach made the method more effective. Techniques for generating a codebook with sparse coding, distance measurement with a Gaussian mixture model and distance measurement between clusters were evaluated and discussed. Also we dynamically calculate the threshold for class voting, which obtained superior results in some cases. Finally, strategies for restricting the actors present in the scenes using optical flow were analyzed. By using the Otsu's method to calculate the threshold from the optical flow at the scene, the effectiveness surpasses our most competitive results: 91.46% accuracy for the Violence in Crowds dataset and 92.79% for the Hockey Fights dataset.

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Anomaly Detection in Surveillance Scenes Using Autoencoders

Joshi,

Patel

2023

SN COMPUT. SCI.

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Global Anomaly Crowd Behavior Detection Using Crowd Behavior Feature Vector

Abstract: In the area of crowd abnormal detection, the parameter of population density, is seldom used to the global crowd behavior detection.

Cited by 8 publications

References 18 publications

Online video-based abnormal detection using highly motion techniques and statistical measures

Online video-based abnormal detection using highly motion techniques and statistical measures

Detection of violent events in video sequences based on the census transform histogram operator

Anomaly Detection in Surveillance Scenes Using Autoencoders

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