Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos

Colque, Rensso Mora; Caetano, Carlos; Schwartz, William Robson

doi:10.1109/sibgrapi.2015.21

Cited by 46 publications

(26 citation statements)

References 19 publications

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“…It models appearance and motion features and thus detects both spatial and temporal anomalies. Histogram of oriented optical flow and oriented gradients [30] is a baseline used in anomaly detection and crowd analysis, [31], [32], [33]. Tracklets are representation of movements in videos and have been applied to abnormal crowd motion analysis [34], [35].…”

Section: Training Samplesmentioning

confidence: 99%

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Kiran

Thomas²,

Parakkal³

2018

J. Imaging

401

153

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Abstract:Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

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Section: Training Samplesmentioning

confidence: 99%

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Kiran

Thomas²,

Parakkal³

2018

J. Imaging

401

153

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“…This descriptor captures information based on the optical flow orientation. The extension of HOOF is the Histograms of Optical Flow Orientation and Magnitude (HOFM) [47], which improves the performance by aggregating the velocity information. To improve the performance, another descriptor called the Histograms of Optical Flow Orientation, Magnitude, and Entropy (HOFME) [46] evaluates the entropy of the orientation flow.…”

Section: Evaluation Metrics and Results Analysismentioning

confidence: 99%

Deep Learning with a Spatiotemporal Descriptor of Appearance and Motion Estimation for Video Anomaly Detection

Gunale

Mukherji²

2018

J. Imaging

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The automatic detection and recognition of anomalous events in crowded and complex scenes on video are the research objectives of this paper. The main challenge in this system is to create models for detecting such events due to their changeability and the territory of the context of the scenes. Due to these challenges, this paper proposed a novel HOME FAST (Histogram of Orientation, Magnitude, and Entropy with Fast Accelerated Segment Test) spatiotemporal feature extraction approach based on optical flow information to capture anomalies. This descriptor performs the video analysis within the smart surveillance domain and detects anomalies. In deep learning, the training step learns all the normal patterns from the high-level and low-level information. The events are described in testing and, if they differ from the normal pattern, are considered as anomalous. The overall proposed system robustly identifies both local and global abnormal events from complex scenes and solves the problem of detection under various transformations with respect to the state-of-the-art approaches. The performance assessment of the simulation outcome validated that the projected model could handle different anomalous events in a crowded scene and automatically recognize anomalous events with success.

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“…Movement orientation is used to recognize events such as running, walking, assembling and spreading. Colque et al [27] capture not only the orientation, but also the magnitude of the flow vectors, which provide information regarding the velocity of the moving objects and improves considerably the representation of the normal event. This novel feature descriptor is called Histograms of Optical Flow Orientation and Magnitude (HOFM).…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

Khraief¹,

Benzarti²,

Amiri³

2019

IJMLC

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Fall detection became a major concern especially for elderly who lives alone at home. Unexpected situations might happen that influence their health, security and well-being. The development of an intelligent surveillance system is required to alleviate the negative effects of unforeseen circumstances and assisting the elderly in independent living. Currently, convolutional neural network has been successfully used for solving various computer vision tasks, such as object detection and recognition. In this paper, we propose a new vision system for elderly fall detection based on new two stream convolutional neural networks. First, human silhouette is extracted based on background subtraction and person recognition. Second, history of binary motion image HBMI is fed into the first stream characterizing the human shape variations. The second stream is based on amplitude and orientation of optical flow defining the velocity and the direction of the human motion. The system classifies fall events using score fusion schema. Transfer learning is performed to deal with the small amount of fall datasets. Our final network outperforms state-of-the-art results on standard fall datasets.

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Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos

Cited by 46 publications

References 19 publications

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Deep Learning with a Spatiotemporal Descriptor of Appearance and Motion Estimation for Video Anomaly Detection

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

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