Detection of dominant flow and abnormal events in surveillance video

Kwak, Sooyeong; Byun, Hyeran

doi:10.1117/1.3542038

Cited by 24 publications

(16 citation statements)

References 11 publications

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“…Various methods of object-based approach have proposed, but when it is applied to complex scenes or scenarios involving large crowds of people, object-based approach has critical weaknesses such as tracking failure or creation of false targets (shadows, clouds, fragmentation of targets, etc.) [2].…”

Section: B Related Workmentioning

confidence: 95%

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Scene Modeling-Based Anomaly Detection for Intelligent Transport System

Kwon

Noh

Jeon

et al. 2013

2013 4th International Conference on Intelligent Systems, Modelling and Simulation

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Recently, in the surveillance field, scene analysis research is a hot topic, and many useful algorithms are developed. They not only reduce human power, but also make surveillance in real time. In this paper, we propose a robust and efficient anomaly-detection algorithm in traffic surveillance system. The proposed method consists of two parts:(1) scene modeling part, and (2) anomaly detection part. First part, to systemically collect trajectories of moving objects, we apply the sparse optical flow method to foreground regions extracted by a conventional background modeling method. These collected trajectories are represented as 3-dimensional feature vectors whose components are x and y coordinates and moving direction, and they are clustered by k-means clustering method. After this process, all feature vectors are assigned clustering labels, and then we construct a trajectory histogram based on cells whose mean grid with a particular size to make the scene model. Then we apply the entropy concept to generated histograms in order to handle some regions where the uncertainty of motion pattern is high. In the anomaly detection part, we get features of objects in a image and track them with the same way in the scene modeling part. At this time, they are classified by nearest neighborhood method. From this result of classification, we can detect anomalies in the traffic video by comparing it with the scene model. Experimental results demonstrate that the anomaly detection rate of the proposed method is very high, and the processing speed is almost real time.

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Section: B Related Workmentioning

confidence: 95%

“…To do that, as mentioned in the previous section, they analyze motion dynamics from object tracking. According to this manner, we can classify the anomaly detection methods into two approaches [2]: objectbased approach, and feature-based approach.…”

Section: B Related Workmentioning

confidence: 99%

Scene Modeling-Based Anomaly Detection for Intelligent Transport System

Kwon

Noh

Jeon

et al. 2013

2013 4th International Conference on Intelligent Systems, Modelling and Simulation

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“…23 It is popular for application papers because the implementation has been well studied and software packages are freely available online. 23 It is popular for application papers because the implementation has been well studied and software packages are freely available online.…”

Section: Basic Lda Formulationmentioning

confidence: 99%

Understanding vehicular traffic behavior from video: a survey of unsupervised approaches

Morris

Trivedi

2013

J. Electron. Imaging

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Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provided.

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“…An algorithm based on collecting low-level statistics is presented in [10]. In [11], [12], a semi-unsupervised learning method based on optical flow was introduced and latent Dirichlet allocation model was used to detect abnormal moving objects. Thus, the external sensors from infrastructure, such as video cameras, are important for video surveillance and cooperative environment perception.…”

Section: Introductionmentioning

confidence: 99%

Event analysis based on multiple video sensors for cooperative environment perception

Chen¹,

Zhu²,

Snoussi³

2015

2015 IEEE International Conference on Progress in Informatics and Computing (PIC)

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Safety is considered as one of the most crucial aspects in the modern transportation domain. In this paper, we benefit from the videos captured by multiple external video sensors from infrastructure, and propose an algorithm to perceive the environment via these data from different aspects. The algorithm consists of two parts: the descriptor for representing the event and the classification method for analyzing the scenes. The covariance matrix feature descriptor is proposed to fuse the optical flow and the intensity of the image, and the nonlinear one-class SVM with a multi-kernel strategy is used to detect the unusual events in the scene. The method is applied to analyze events in the video surveillance dataset with promising results obtained.

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Detection of dominant flow and abnormal events in surveillance video

Cited by 24 publications

References 11 publications

Scene Modeling-Based Anomaly Detection for Intelligent Transport System

Scene Modeling-Based Anomaly Detection for Intelligent Transport System

Understanding vehicular traffic behavior from video: a survey of unsupervised approaches

Event analysis based on multiple video sensors for cooperative environment perception

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