Behavior Analysis and Dynamic Crowd Management in Video Surveillance System

Szczodrak, Maciej; Kotus, Józef; Kopaczewski, Krzysztof; Lopatka, Kuba; Czyżewski, Andrzej; Krawczyk, Henryk

doi:10.1109/dexa.2011.16

Cited by 21 publications

(7 citation statements)

References 9 publications

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“…It is most commonly used and the best fit for data having an equal number of samples for positive and negative classes however is not effective for imbalance data modeling. Similarly, the false positive rate [58], also known as fall-out, calculates the proportion of negative samples incorrectly classified as positive samples. It is mostly utilized to check the false alarm rates of an AI model [83].…”

Section: Performance Evaluation Of Fuzzy Based Surveillance Bvd Analyticsmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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Section: Performance Evaluation Of Fuzzy Based Surveillance Bvd Analyticsmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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“…In the first category we distinguish the methods based on the analysis of behavior [7][8][9][10][11]. These methods supply an interesting static analysis of the surveillance of the crowds but do not detect abnormal events.…”

Section: State Of the Art And Approach Descriptionmentioning

confidence: 99%

“…The goal of this approach is to illustrate the detection of anomalies in very dense scenes based on the speed of the individuals and that of the group. The various anomalies are detected automatically by dynamic switching between two approaches which are the artificial neural networks for the management of anomalies in a group of people, while the DBSCAN method is used to detect the entities [8,9]. For greater robustness and effectiveness, we have introduced two routines allowing the elimination of the shades [10,11] and the management of occlusions [11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic detection of abnormalities in video analysis of crowd behavior with DBSCAN and neural networks

Chebi¹,

Acheli²,

Kesraoui³

2016

Adv. sci. technol. eng. syst. j.

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Visual analysis of human behavior is a broad field within computer vision. In this field of work, we are interested in dynamic methods in the analysis of crowd behavior which consist in detecting the abnormal entities in a group in a dense scene. These scenes are characterized by the presence of a great number of people in the camera's field of vision. The major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by human operators. We present in this paper a new approach for the detection of dynamic anomalies of very dense scenes measuring the speed of both the individuals and the whole group. The various anomalies are detected by dynamically switching between two approaches: An artificial neural network (ANN) for the management of group anomalies of people, and a Density-Based Spatial Clustering of Application with Noise (DBSCAN) in the case of entities. For greater robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions. The two latter phases have proven that the results of the simulation are comparable to existing work.

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“…The availability of low cost sensors, processors and the need for safety and security at public sectors are the main reasons for this emerging interest in research for visual surveillance systems. Visual surveillance systems generally consist of four main steps; image acquisition, image pre-processing, background modelling and behaviour understanding [2]. Therefore making background modelling a key step towards behaviour understanding applications of crowd analytic surveillance systems.…”

Section: Introductionmentioning

confidence: 99%

Reliability of bench-mark datasets for crowd analytic surveillance

Hassan

Malik

Walter

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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This paper presents an evaluation on the reliability of the bench-mark datasets for outdoor crowd analytic surveillance systems. The credibility of the databases are assessed based on their diverseness to yield challenges of dynamic environments. The main object of this paper is to assess the challenges imposed by the databases for sudden illumination variance and effect of wavering trees.Two bench-mark databases, PETS 2010 and OTCBVS, along with our proposed dataset are evaluated using the three most popular background modelling algorithms in crowd analytic surveillance; Approximate Median Method, Gaussian Mixture Model and Codebook. The diverseness of these databases are assessed, with respect to the performance of the basic algorithms using qualitatively and quantitatively. Eventually the reliability of these bench-mark databases for outdoor crowed analytic surveillance is assessed.

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Behavior Analysis and Dynamic Crowd Management in Video Surveillance System

Cited by 21 publications

References 9 publications

Fuzzy Logic in Surveillance Big Video Data Analysis

Fuzzy Logic in Surveillance Big Video Data Analysis

Dynamic detection of abnormalities in video analysis of crowd behavior with DBSCAN and neural networks

Reliability of bench-mark datasets for crowd analytic surveillance

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