Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy

Zhang, Xuguang; Lin, D.M.; Zheng, Juan; Tang, Xianghong; Fang, Yinfeng; Yu, Hui

doi:10.3390/e21060608

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Crowd movement has strong physical properties, so many works have applied physical models to analyze crowd videos [37]. Based on the physics properties, the methods of crowd behavior analysis can be expressed as fluid dynamics [38], energy, entropy models [39] and complex system science [40]. Among them, the fluid dynamics model has been widely used.…”

Section: Related Workmentioning

confidence: 99%

“…Other methods are based on the fluid force model. Zhang et al [39] calculated the repulsive force based on the crowd vector field to construct a weighted network for salient crowd motion detection. Smoothed Particle Hydrodynamics (SPH), which was originally proposed by Gingold [31], is a meshless method for studying the motion characteristics of fluid particles.…”

Section: Related Workmentioning

confidence: 99%

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Scene perception guided crowd anomaly detection

Zhang

et al. 2020

Neurocomputing

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Scene perception guided crowd anomaly detection

Zhang

et al. 2020

Neurocomputing

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“…They used CNN using self-attention mechanism so as to find the salient areas in human crowd images. Similarly, Zhang et al [ 106 ] were able to detect salient crowd motion using direction entropy and a repulsive force network. The frames of the crowd video sequence are evaluated by an optimal flow technique.…”

Section: Approachesmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Khan

Albattah

Khan

et al. 2020

Sensors

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Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

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“…[ 24 ] used divergence measures, such as Kullback–Leibler (KL), between distributions of the data computed at different times in order to detect anomalies in big data streams. In [ 25 ], the authors suggested an entropy-based method to detect salient points in a crowd. This time, the entropy is used to measure the uncertainty of a particle motion direction, hence describing the chaotic degree of crowd motion.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools

Siboni

Cohen

2020

Entropy

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Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources.

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Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy

Cited by 12 publications

References 22 publications

Scene perception guided crowd anomaly detection

Scene perception guided crowd anomaly detection

Advances and Trends in Real Time Visual Crowd Analysis

Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools

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