Real-time crowd motion analysis

Ihaddadene, Nacim; Djeraba, Chabane

doi:10.1109/icpr.2008.4761041

Cited by 96 publications

(51 citation statements)

References 9 publications

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“…For instance, some methods are based on motion analysis: in [3] a technique based on the observation of the motion of particles is described; such particles are initially evenly placed over the image domain, and then moved according to optical flow. In [4] a technique based on motion heat maps is presented, together with a set of indicators for measuring motion entropy and, finally, classifying motion as normal or abnormal. Finally, in [5] a method is presented, that is capable of detecting the precise contour of a crowd.…”

Section: Related Workmentioning

confidence: 99%

Texture-Based Crowd Detection and Localisation

Ghidoni¹,

Cielniak

Menegatti³

2013

Advances in Intelligent Systems and Computing

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This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation.

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Section: Related Workmentioning

confidence: 99%

Texture-Based Crowd Detection and Localisation

Ghidoni¹,

Cielniak

Menegatti³

2013

Advances in Intelligent Systems and Computing

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“…To address these limitat ions, some authors have recently proposed learning methods based on charac teristics other than motion paths [8]. In such a case, there is no need for object tracking, instead, we consider pixel -level features.…”

Section: Introductionmentioning

confidence: 99%

Video Anomaly Detection Using Block Based Approach

2015

IJAERD

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“…Early studies, such as the "Minkowski fractal dimension" model [1] and the flow-based "crowd motion" model [2], had focus on the extraction of crowd attributes from the flows such as density, moving direction, size and boundaries. In recent years, more attention has shifted towards applicationoriented techniques to improve the flow-based crowd pattern interpretation [3][4][5]. For example, in 2007, Ali [6] first introduced a crowd scene model based on "finite time Lyapunov exponent field" -an extension of the flowfiled model -for segmenting extremely dense crowd scenes recorded in videos.…”

Section: Introductionmentioning

confidence: 99%

Texture-based homogeneity analysis for crowd scene modelling and abnormality detection

Wang

2014

2014 20th International Conference on Automation and Computing

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Abstract-Video-based crowd behaviour analysis techniques aim at tackling challenging problems such as detecting abnormal crowd behaviours and tracking specific individuals from complex real life scenes. In this paper, an innovative spatio-temporal texture-based crowd modelling technique and its corresponding pattern analysis methods have been introduced. Through extracting and integrating those crowd textures from live or recorded videos, the so-called homogeneous random features have been deployed in the research for behavioural template matching. Experiment results have shown that the abnormality appearing in crowd scenes can be effectively and efficiently identified by using the devised methods. This new approach is envisaged to facilitate a wide spectrum of crowd analysis applications in the future through laying a solid theoretical foundation and implementation strategy for automating existing Closed-Circuit Television (CCTV)-based surveillance systems.

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Real-time crowd motion analysis

Cited by 96 publications

References 9 publications

Texture-Based Crowd Detection and Localisation

Texture-Based Crowd Detection and Localisation

Video Anomaly Detection Using Block Based Approach

Texture-based homogeneity analysis for crowd scene modelling and abnormality detection

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