Crowd panic state detection using entropy of the distribution of enthalpy

Zhang, Xuguang; Shu, Xiaohu; He, Zhen

doi:10.1016/j.physa.2019.04.033

Cited by 33 publications

(18 citation statements)

References 26 publications

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“…Another idea developed in several works [2,10,11,12,13] is to evaluate the global entropy of a crowd. Entropy can be used to evaluate how much a crowd movement is choice-less or not.…”

Section: Related Workmentioning

confidence: 99%

“…The first type of disorder we intend to evaluate is related to the actual motion, and it relates a collective motion to pedestrians walking with the same velocity and in the same direction. Our contribution regarding this term is that we conduct a local evaluation, and prove the interest of inferring locally, in contrast to several works [2,10,11,12,13]. For the second type of disorder, we propose to study to what extent people look the same, hence we speak of disorder of appearance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Crowd Behavior Characterization for Scene Tracking

Franchi¹,

Aldea²,

Dubuisson³

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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In this work, we perform an in-depth analysis of the specific difficulties a crowded scene dataset raises for tracking algorithms. Starting from the standard characteristics depicting the crowd and their limitations, we introduce six entropy measures related to the motion patterns and to the appearance variability of the individuals forming the crowd, and one appearance measure based on Principal Component Analysis. The proposed measures are discussed on synthetic configurations and on multiple real datasets. These criteria are able to characterize the crowd behavior at a more detailed level and may be helpful for evaluating the tracking difficulty of different datasets. The results are in agreement with the perceived difficulty of the scenes.

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“…Another idea developed in several works [2,10,11,12,13] is to evaluate the global entropy of a crowd. Entropy can be used to evaluate how much a crowd movement is choice-less or not.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crowd Behavior Characterization for Scene Tracking

Franchi¹,

Aldea²,

Dubuisson³

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

View full text Add to dashboard Cite

show abstract

“…But the long-time static target is difficult to be detected out accurately. Zhang et al (2018Zhang et al ( , 2019, Afiq et al (2019) applied dynamic target detection to crowd action and emotion recognition. In addition, the lack of laser radar can bring instable judgment for some obstacles with special material (such as glass doors), which can affect the accuracy of the map (Einhorn and Gross, 2015), achieved estimation tracking of the dynamic object by combining the normal distribution transformation with the grid map.…”

Section: Related Workmentioning

confidence: 99%

Walking Human Detection Using Stereo Camera Based on Feature Classification Algorithm of Second Re-projection Error

et al. 2019

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This paper presents a feature classification method based on vision sensor in dynamic environment. Aiming at the detected targets, a double-projection error based on orb and surf is proposed, which combines texture constraints and region constraints to achieve accurate feature classification in four different environments. For dynamic targets with different velocities, the proposed classification framework can effectively reduce the impact of large-area moving targets. The algorithm can classify static and dynamic feature objects and optimize the conversion relationship between frames only through visual sensors. The experimental results show that the proposed algorithm is superior to other algorithms in both static and dynamic environments.

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“…As video monitoring can obtain the information of the scene, and monitor, alarm, record, and query the crowd status, crowd video monitoring plays an important role in the field of crowd behavior analysis. Many contributions have been proposed for crowd video monitoring such as target detection and tracking [ 13 , 14 ], crowd counting and density estimation [ 15 , 16 ], and crowd abnormal behavior detection [ 17 , 18 ]. However, traditional crowd video monitoring methods are often limited in the field of crowd counting and other feature detection, or to detect the abnormal and disaster events that have occurred.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

Zhang

Wang

2020

Entropy

Self Cite

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Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian’s number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian’s number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian’s number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian’s number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

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Crowd panic state detection using entropy of the distribution of enthalpy

Cited by 33 publications

References 26 publications

Crowd Behavior Characterization for Scene Tracking

Crowd Behavior Characterization for Scene Tracking

Walking Human Detection Using Stereo Camera Based on Feature Classification Algorithm of Second Re-projection Error

Fuzzy Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

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