Real-time crowd behavior detection using SIFT feature extraction technique in video sequences

Choudhary, Shivali; Ojha, Nitish Kumar; Singh, Vrijendra

doi:10.1109/iccons.2017.8250602

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…However, the system is not effective for arbitrary movements or overlaps. S. Choudhary et al in [ 78 ] proposed a SIFT feature extraction technique, along with a Genetic Algorithm for optimal feature extraction; anomalies were detected by checking feature set movement behaviors. Their proposed system has a very high computational processing demand.…”

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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“…Technicolor [23] and Mediaeval [24], [25] on the basis of evaluation performed by a consortium of experts. • the proposal of feature extraction and learning for certain specific violence facts, for example the violence: induced by the crowd [26], [27], [28], [29] , the following Section IV-B proposes two experimental benchmarking frameworks: the first considers, in a joint framework, both VSD@Mediaeval and VSD@Technicolor. The second proposes upgrading the database obtained by fusing VSD@Mediaeval and VSD@Technicolor in order to derive a 3-level violence category database.…”

Section: Visual Violence Detection In Videos: the Hilbert Image Fmentioning

confidence: 99%

Timed-image based deep learning for action recognition in video sequences

Atto

Benoît

Lambert

2020

Pattern Recognition

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“…There are two types of crowd namely structured crowd and unstructured crowd. In the former, the direction of the movement is towards a common point and people are not in scattered form while in the later type the direction of the people is not towards a common point and they are usually in scattered form [1].…”

Section: Introductionmentioning

confidence: 99%

“…For localization in crowded scene, density map has been used as a regularizer during the detection [27]. In computer vision, crowd behavior detection in video surveillance is one of the latest research areas [1]. Crowd behavior detection has many application domains such as automatic detection of riots or chaotic acts in crowd and localization of abnormal regions [28].…”

Section: Introductionmentioning

confidence: 99%

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.

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Real-time crowd behavior detection using SIFT feature extraction technique in video sequences

Cited by 12 publications

References 9 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

Timed-image based deep learning for action recognition in video sequences

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

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