Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

Hu, Xing; Hu, Shiqiang; Zhang, Xiaoyu; Zhang, Huanlong; Luo, Lingkun

doi:10.1155/2014/632575

Cited by 17 publications

(9 citation statements)

References 42 publications

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“…Yuan et al [26] exploited contextual evidence using a structural context descriptor (SCD) to describe the relationship of individuals. Hu et al [27] proposed a compact and efficient local nearest neighbors distance (LNND) descriptor to incorporate the spatial and temporal contextual information around a video event for AED. In fact, contextual information is an important cue for AED since it reflects the co-occurrence relationships or macro-structural information among semantic descriptors.…”

Section: ) Context-wise Descriptormentioning

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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Section: ) Context-wise Descriptormentioning

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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“…Meanwhile, several attempts have also been made to find anomalies using topic models and surveillance cameras [24,25]. Jeong et al [26] proposed a topic model for detecting anomalous trajectories of people or vehicles in surveillance-video images.…”

Section: Related Workmentioning

confidence: 99%

A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

2018

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Abstract:The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection.

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“…Xing Hu et al [20] proposed a LNND descriptor to represent the video event for anomaly detection in crowded scenes.Si Wu et al [19] proposed a Bayesian framework for escape detection by directly modeling crowd motion in both the presence and absence of escape events. Specifically, they introduced the concepts of potential destinations and divergent centers to characterize crowd motion in the above two cases respectively,and construct the corresponding classconditional probability density functions of optical flow.Chunyu Chen et al [18] proposed an algorithm based on the acceleration feature to detect anomalous crowd behaviors in video surveillance systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

Kumar¹,

Bhatnagar²

2017

IJCIS

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In the current era, crowd behavior analysis is important topic due to the significance of video surveillance in the public area. Literature presents a handful of works for crowd behavior detection and analysis. Even though, the complicated challenges such as, low quality video, wide variation in the density of crowds and difficult motion patterns pose a complicated challenges for the researchers in crowd behavior detection. In order to alleviate these issues, we develop a crowd behavior detection system using hybrid tracking model and integrated features enabled neural network. The proposed crowd behavior detection system estimate the direction of movement of objects as well their activity using proposed GLM-based neural network. The proposed GLM-based neural network integrates the LM algorithm with genetic algorithm to improve the learning process of neural network. The performance of the proposed crowd behavior detection algorithm is validated with five different video and the performance is extensively analyzed using accuracy. From research outcome, we proved that the proposed system obtained the maximum accuracy of 95% which is higher than the existing methods taken for comparison.

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Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

Cited by 17 publications

References 42 publications

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

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

A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

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