Motion pattern analysis using partial trajectories for abnormal movement detection in crowded scenes

Bae, Guntae; Kwak, Sooyeong; Byun, Hyeran

doi:10.1049/el.2012.3340

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…The combination of two features is used in this paper for latter recognition: the size feature and the color histogram. The size feature is computed based on area, and the color histogram is computed as Equation ( 1) and Equation (2).…”

Section: Occlusion Processingmentioning

confidence: 99%

“…There has been a fair amount of research conducted on action recognition. For the task of action recognition or classification, recent approaches use features based on optical flow [12], grammars [9], motion analysis [2,8], spatiotemporal interest points [10], or dense point trajectories extracting from regions [11], which are being tracked using optical flow across the frames. To improve processing efficiency for videos on a larger scale [13], deep learning tools such as convolutional neural networks [3,4,6,7] have been applied.…”

Section: Introductionmentioning

confidence: 99%

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Group Behavior Recognition in Videos based on Cam-Shift Tracking and Histogram Changing Rate

Liu¹

2018

IJPE

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With more and more cameras installed in public places, video surveillance systems play an increasingly important role in public safety. Research on intelligent video monitoring, especially activity recognition, is attracting increasing attention in the field of image processing. Unlike activity recognition of a single tracking object, group activity is more complex and difficult to recognize. To design a fast realtime group activity recognition algorithm without other auxiliary data, low computational cost is our focus. There are four steps for our group activity recognition system: preprocessing the captured videos, extracting foregrounds from backgrounds, tracking multiple objects and recognizing group activity. To remove noise in each frame image, the combination of the Gaussian filter algorithm and median filter algorithm is used in the preprocessing step. Then, the Gaussian mixture model is adopted to extract the foreground image. To ensure low computational cost, real-time Cam-Shift is chosen to track group activity with morphological operations in the tracking step. In the recognition step, the changing histogram rate is defined as the measure of identifying group behavior. Here, the changing histogram rate refers to the number of changing histograms and changing proportions. Experimental results show that the group activity recognition algorithm proposed in this paper is effective with low computational cost.

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Section: Occlusion Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Group Behavior Recognition in Videos based on Cam-Shift Tracking and Histogram Changing Rate

Liu¹

2018

IJPE

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“…Bae et al [29] have presented a technique of identifying abnormal mobility of an object using partial trajectory-based information from the cluttered scene. However, the accuracy is not found to have better improvement irrespective of a better approach.…”

Section: Related Techniquesmentioning

confidence: 99%

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

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Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

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“…In [17], ORB (oriented FAST and rotated BRIEF) features are used to represent the motion behaviour in overlapping spatial zones which are then modelled using a coupled HMM. In [18], Kanade-Lucas-Tomasi feature tracking is used to obtain partial trajectories of corners. Then these trajectories are grouped into visual words based on their motion characteristics and a dictionary is obtained.…”

Section: Related Workmentioning

confidence: 99%

Density aware anomaly detection in crowded scenes

Gunduz

Öngün

Temizel

et al. 2016

IET Computer Vision

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Coherent nature of crowd movement allows representing the crowd motion using sparse features. However, surveillance videos recorded at different periods of time are likely to have different crowd densities and motion characteristics. These varying scene properties necessitate use of different models for an effective representation of behaviour at different periods. In this study, a density aware approach is proposed to detect motion‐based anomalies for scenes having varying crowd densities. In the training, the sparse features are modelled using separate hidden Markov models, each of which becomes an expert for specific scene characteristics. These models are then used for anomaly detection. The proposed method automatically adapts to the changing scene dynamics by switching to the most representative model at each frame. The authors demonstrate the effectiveness and real‐time performance of the proposed method on real‐life datasets as well as on simulated crowd videos that they generated and made publicly available to download.

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Motion pattern analysis using partial trajectories for abnormal movement detection in crowded scenes

Cited by 6 publications

References 4 publications

Group Behavior Recognition in Videos based on Cam-Shift Tracking and Histogram Changing Rate

Group Behavior Recognition in Videos based on Cam-Shift Tracking and Histogram Changing Rate

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Density aware anomaly detection in crowded scenes

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