Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Xu, Jingxin; Denman, Simon; Sridharan, Sridha; Fookes, Clinton; Rana, Rajib

doi:10.1145/2072508.2072515

Cited by 36 publications

(29 citation statements)

References 14 publications

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“…It represents video sequences as observations from a linear dynamical system, and exhibits spatio-temporal stationary properties [91]. Recent research works [80], [87] have shown that dynamic texture is more suitable for local unusual event detection in crowded scenes than optical flow.…”

Section: Capturing Temporal Statistics In Distribution Based Hidden Mmentioning

confidence: 99%

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Crowded Scene Analysis: A Survey

Chang

Wang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

452

201

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Abstract-Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.

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Section: Capturing Temporal Statistics In Distribution Based Hidden Mmentioning

confidence: 99%

“…Combined with dynamic texture, Xu et al [91] proposed a novel approach for unusual event detection via sparse reconstruction on an over-complete basis set. The dynamic texture is described by local binary patterns from three orthogonal planes (LBPTOP).…”

Section: Normalcy and Anomaly Modelingmentioning

confidence: 99%

Crowded Scene Analysis: A Survey

Chang

Wang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

452

201

View full text Add to dashboard Cite

show abstract

“…These figures show that our approaches can well detect the global abnormal event of people quickly dispersing and achieve high performance. To evaluate the performances of our approaches, we compared our approaches with the approaches of PSO-SF [46], LBP-TOP [47], optical flow [47], and DBM [48] in Table 5. The comparison demonstrates that our performances were better than or comparable to those of the state-of-the-art approaches.…”

Section: Pets2009mentioning

confidence: 99%

Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor

Duan

et al. 2018

EURASIP J. Adv. Signal Process.

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Detecting abnormal events in crowded scenes is an important but challenging task in computer vision. Contextual information is useful for discovering salient events in scenes; however, it cannot be characterized well by commonly used pixel-based descriptors, such as the HOG descriptor. In this paper, we propose contextual gradients between two local regions and then construct a histogram of oriented contextual gradient (HOCG) descriptor for abnormal event detection based on the contextual gradients. The HOCG descriptor is a distribution of contextual gradients of sub-regions in different directions, which can effectively characterize the compositional context of events. We conduct extensive experiments on several public datasets and compare the experimental results using state-of-the-art approaches. Qualitative and quantitative analysis of experimental results demonstrate the effectiveness of the proposed HOCG descriptor.

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“…It represents video sequences as observations via a linear dynamical system and exhibits spatio-temporal stationary properties (Raghavendra et al, 2011a;Li et al, 2014;Chetverikov and Péteri, 2005;Xu et al, 2011). Recent research works (Chan and Vasconcelos, 2008) have shown that dynamic texture is more suitable for local unusual event detection in crowded scenes than optical flow.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

Xue

Liu

Tao

et al. 2017

International Journal of Applied Mathematics and Computer Science

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In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.

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Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Cited by 36 publications

References 14 publications

Crowded Scene Analysis: A Survey

Crowded Scene Analysis: A Survey

Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

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