Abnormal Event Detection in Surveillance Videos Using Two-Stream Decoder PDF Download
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Author: 梁榮發
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
Author: 梁榮發
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
Author: Xiaochun Wang
Publisher: Springer Nature
ISBN: 9819730236
Category :
Languages : en
Pages : 396
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Book Description
Author: Mei Kuan Lim
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 338
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Author: Roberto Leyva
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Marcial Roberto Leyva Fernandez
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 163
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Author: Tian Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM).
Author: Arun Kumar H. D.
Publisher:
ISBN: 9781835800812
Category :
Languages : en
Pages : 0
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Author: Fan Jiang
Publisher: LAP Lambert Academic Publishing
ISBN: 9783844309645
Category :
Languages : en
Pages : 96
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Content-based video analysis serves as the cornerstone for many applications: video understanding or summarization, multimedia information retrieval and data mining, etc. In our research, we aim to automatically detect anomalous events from surveillance videos (such as video monitoring traffic flow or pedestrian congestion in public spaces). Conceptually, what constitutes an anomaly varies in different video scenarios and is difficult to be defined in a general case. Our first solution is based on unsupervised clustering of object trajectories and anomalous trajectory identification in a probabilistic framework. Then we extend this solution to an arbitrary time length (any part of a complete trajectory) and multiple objects (multiple trajectories). Furthermore, we solve problems specifically in video scenarios where object trajectories cannot be extracted (e.g., crowd motion analysis). Our contributions include a novel hierarchical clustering algorithm and categorization of anomalous video events by spatiotemporal context.
Author: Ricardo Augusto Castellanos Jimenez
Publisher:
ISBN:
Category : Computer security
Languages : en
Pages : 182
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Book Description
Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%.
Author: Seby Jacob
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"This thesis proposes an innovative solution to detect and localize anomalous events in a video stream from a static camera. Anomalies are defined as events with a very low probability of occurrence in the scene or as events typically uncharacteristic of the scene. In this work, we employ a constrained convolutional auto-encoder to learn the scene characteristics. The autoencoder is trained on spatio-temporal video-volumes extracted from recorded videos of the scene. Once the training is complete, each incoming video-volume can be tested for its anomalous nature by analyzing the low-dimensional encodings and the quality of its reconstruction from the auto-encoder. Anomalies are heavily subjective to the scene being monitored. The most abnormal event in one scene could be the most normal event in another. Hence, special care has been taken to make the solution applicable for any scenario. Since training is unsupervised, this work is extremely general purpose and can be deployed on any scene as is. Apart from the discourse on a novel solution that is competitive with state-of-the-art methods, this work also has an additional contribution. Specifically, we present a framework for generating unlimited amounts of video data for anomaly detection from a static camera. This enables the evaluation of any deep learning models, that were previously not adaptable for the problem due to the limited training data available in benchmark datasets. We present results from extensive experimentation on popular benchmark datasets to show that our solution is effective and robust for anomaly detection. We also establish the importance of having sufficient training data via the evaluation of models trained on training- sets of varying sizes. Finally, the idiosyncratic nature of "What is an anomaly?" is subjected to analysis using an experimental methodology." --
