Statistical Abnormal Crowd Behavior Detection and Simulation for Real-Time Applications

Aguilar, Wilbert G.; Luna, Marco A.; Ruiz, Hugo; Moya, Julio F.; Luna, Marco P.; Abad, Vanessa; Parra, Humberto

doi:10.1007/978-3-319-65292-4_58

Cited by 18 publications

(2 citation statements)

References 35 publications

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“…Direkoglu et al in [ 79 ] used a one-class SVM, along with features based on optical flow to detect crowd behavior; their system is limited by the accuracy limitations of optical flow estimation. W. G. Aguilar et al in [ 80 ] introduced a moved-pixels density-based statistical modeling approach for detecting abnormal crowd behavior. This system has low computational cost, but the efficiency decreases with increasing complexity of the situation being monitored, e.g., serious occlusions.…”

Section: Related Workmentioning

confidence: 99%

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Abdullah

Ghadi

Gochoo

et al. 2021

Entropy

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To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

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Section: Related Workmentioning

confidence: 99%

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Abdullah

Ghadi

Gochoo

et al. 2021

Entropy

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“…A pedestrian detection in the abnormal crowd was proposed by Aguilar [12]. The motion detection analysis process is initially carried out.…”

Section: Review On the Year 2017mentioning

confidence: 99%

A Review on Pedestrian Detection in the Vehicle Dashpot and Surveillance Camera Video Footage

Thiyaneswaran¹,

Anguraj²,

Keerthana³

et al. 2018

IJCA

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Automation plays an important role in the universe. The automation is having more impact in vehicle. A pedestrian detection is required for advance d driver assistance system and security surveillance system. The video is captured using camera which may fit in the vehicle dashpot or CCTV footage fitted in home or industry. The videos are converted into frames. The frames are analyzed and further pedestrian is detected using image processing techniques. In this paper, the review is carried out using the recent research work. Around 20 numbers of papers are taken for the review. The various techniques used in the pedestrian detection such as histogram oriented gradient method, SURF, SIFT, LDA are gathered using the review. The various classifiers required to identify the person or detecting the pedestrian are analyzed. The various hardware implementations used in the recent research is discussed. The recently achieved accuracy and error rate are analyzed using the review.

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R-3RPS Robot-Based Mathematical Modeling for a Military Flight Simulator

Guerrón

Aguilar

Reyes

et al. 2019

Intelligent Robotics and Applications

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Statistical Abnormal Crowd Behavior Detection and Simulation for Real-Time Applications

Cited by 18 publications

References 35 publications

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

A Review on Pedestrian Detection in the Vehicle Dashpot and Surveillance Camera Video Footage

R-3RPS Robot-Based Mathematical Modeling for a Military Flight Simulator

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