Academic literature on the topic 'Abnormal Event Detection'

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Dissertations / Theses on the topic "Abnormal Event Detection"

1

Leyva, Roberto. "Online video analysis for abnormal event detection and action recognition." Thesis, University of Warwick, 2018. http://wrap.warwick.ac.uk/104211/.

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Automatic video surveillance has become one of the most active research areas in computer vision. Its applications are vast; these include security purposes, patient monitoring and law enforcement. Considering that millions of cameras operate all over the world, human surveillance is impractical for many reasons. Perhaps the most important reason is that strictly speaking, we require one person to monitor one camera. This monitoring is not only unrealistic but also inefficient because we cannot have a person 24/7 observing a scene. Even if that would be possible, fatigue and distractions might deter its efficiency. The main challenge of video surveillance is that it requires online processing (no-cumulative delay process) for practical scenario purposes. The reason is that the system’s response should be given immediately after the event occurred. If this time requirement is not satisfied, the system will end up warning the operators minutes or hours later. Then, the system’s response will be impractical for some events (e.g. crimes, accidents and fires) where the response times are critical. Although many methods have been developed for video surveillance, there is very little in terms of online-based methods. The lack of online approaches has been because there is a trade-off between accuracy in detecting events and computational complexity. The objective of this thesis is to minimise the gap of the speed-accuracy trade-off. To this end, this thesis proposes: (I) multi-source motion extraction to boost accuracy and expand the type of events to be detected, (II) extract few but high descriptive features via multi-scale extraction with perspective compensation, and (III) four fast binary-based video descriptors. The main findings of this thesis are as follows: First, multi-scaled perspective features reduce computational times meeting online requirements in abnormal event detection. Second, binary video features achieve competitive accuracy in action recognition compared with existing features while drastically outperform them in terms of computational complexity. In conclusion, first, by carefully selecting the spatio-temporal regions to process video data significantly improves accuracy and at the same time reduces computational times to detect abnormal events. Second, binary video features can compete with existing features by selecting a limited number of descriptive spatio-temporal symmetric regions. Finally, the findings of this thesis could benefit all those video applications that require real-time or online processing times.
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Ramesh, Rohit. "Abnormality detection with deep learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118542/1/Rohit_Ramesh_Thesis.pdf.

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This thesis is a step forward in developing the scientific basis for abnormality detection of individuals in crowded environments by utilizing a deep learning method. Such applications for monitoring human behavior in crowds is useful for public safety and security purposes.
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Nallaivarothayan, Hajananth. "Video based detection of normal and anomalous behaviour of individuals." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106947/1/Hajananth_Nallaivarothayan_Thesis.pdf.

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This PhD research has proposed novel computer vision and machine learning algorithms for the problem of video based anomalous event detection of individuals. Varieties of Hidden Markov Models were designed to model the temporal and spatial causalities of crowd behaviour. A Markov Random Field on top of a Gaussian Mixture Model is proposed to incorporate spatial context information during classification. Discriminative conditional random field methods are also proposed. Novel features are proposed to extract motion and appearance information. Most of the proposed approaches comprehensively outperform other techniques on publicly available datasets during the time of publications originating from the results.
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Nawarathna, Ruwan D. "Detection of Temporal Events and Abnormal Images for Quality Analysis in Endoscopy Videos." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283849/.

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Recent reports suggest that measuring the objective quality is very essential towards the success of colonoscopy. Several quality indicators (i.e. metrics) proposed in recent studies are implemented in software systems that compute real-time quality scores for routine screening colonoscopy. Most quality metrics are derived based on various temporal events occurred during the colonoscopy procedure. The location of the phase boundary between the insertion and the withdrawal phases and the amount of circumferential inspection are two such important temporal events. These two temporal events can be determined by analyzing various camera motions of the colonoscope. This dissertation put forward a novel method to estimate X, Y and Z directional motions of the colonoscope using motion vector templates. Since abnormalities of a WCE or a colonoscopy video can be found in a small number of frames (around 5% out of total frames), it is very helpful if a computer system can decide whether a frame has any mucosal abnormalities. Also, the number of detected abnormal lesions during a procedure is used as a quality indicator. Majority of the existing abnormal detection methods focus on detecting only one type of abnormality or the overall accuracies are somewhat low if the method tries to detect multiple abnormalities. Most abnormalities in endoscopy images have unique textures which are clearly distinguishable from normal textures. In this dissertation a new method is proposed that achieves the objective of detecting multiple abnormalities with a higher accuracy using a multi-texture analysis technique. The multi-texture analysis method is designed by representing WCE and colonoscopy image textures as textons.
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Wang, Chun-Hui, and 王春暉. "Abnormal Event Detection for Crowd Behavior." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/95486500144888142811.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>101<br>In this paper a simple and effective crowd behavior normality method is proposed. Feature vector, so called HOSF (histogram of oriented social force), and consists of concatenating local histogram of oriented social force. A dictionary of codewords is trained to include typical HOSF. To detect whether an event is normal is accomplished by comparing how similar to the closest codeword via z-value. The proposed method includes the following characteristic: (1) the training is automatic instead of human labeling; (2) instead of object tracking, the method integrates particles and social force as feature descriptors which well adapted in both crowded or few people scenes; (3) z-score is used in measuring the normality of events. Due to computation simplicity, the normality detection could be real-time once the training is finished.
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6

Chien, I.-Feng, and 簡翊峰. "Spatio-Temporal Networks for Abnormal Event Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/m84ak7.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>Abnormaleventscancauseeconomiclossesandcasualties. Beingabletodetectabnormal events in the early stage is the only way to avoid significant losses. In our thesis, we present a method of using cameras to detect abnormal events in time, which can work in different environments. Abnormal events usually only appear in a small area of the image,soweuseatwo­stagearchitecturetoimproveaccuracy. Inthefirstphase,weuse the spatio­temporal network to find the area that might be a abnormal events from the video. Inthesecondstage,WeuseResnettodeterminewhethertheareaisnormalornot. Our experimental results show that the proposed method has better accuracy than other methods.
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Chun-Ku, Lee. "Abnormal Event Detection in Video using N-cut Clustering." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0016-1303200709291362.

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Lee, Chun-Ku, and 李俊谷. "Abnormal Event Detection in Video using N-cut Clustering." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/65919418740000150803.

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碩士<br>國立清華大學<br>電機工程學系<br>94<br>Imagine you are asked to find out an unusual event in a daily recorded surveillance video. Questions aroused, how to detect events in a variety scenes? We focus our attention on finding out events that difference most from others and report it for further examinations. First we divide a video into several overlapping clips. Then we use optical flow to find out motion vectors of each frame in each clip. Magnitudes histogram, direction histogram and color histogram are selected as features. We form a similarity matrix by using difference and chamfer difference as the similarity measure of features in different clips. Then, we apply n-cut clustering .A threshold is selected to balance FAR (false alarm rate) and THR (true hit rate) according to ROC curve (receiver operating characteristic) and once a threshold is selected , clusters correspond to low self-similarity value is reported as unusual events and for further examination. Finally, this mechanism is tested on 6 different views.
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Huang, Chin-Kuei, and 黃致魁. "Vision-Based Abnormal Event Detection System Using Mobile Robot." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/93795393578838607396.

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碩士<br>元智大學<br>電機工程學系<br>97<br>This thesis proposes a system which aims at developing methodologies and techniques for abnormal event detection and navigation of a surveillance mobile robot. In the system, this approach can be divided into two parts, i.e., abnormal event detection and mobile robot navigation, for scene representation and exceptional change detection of important like paintings or antiques when mobile robot navigating known environment. In abnormal event detection, the operator controls the mobile robot to collection different videos for scene representation of training phase. Then, we use a method to build the background scene that is a patch-based technique. For detecting the abnormal event, in order to detect the abnormal object quickly, we use a patch searching algorithm that is present for scene registration. Therefore, that all possible exceptional changed that can be very efficiently detected form the scene panorama. In mobile robot navigation, the database is built as like the abnormal event detection system that let registers the path information of the mobile robot navigates known environment. Then, we use scene matching algorithm to compute the mobile robot direction and then guiding to the correct path. Experimental results are conducted to illustrate the feasibility and efficacy of the mobile robot of surveillance system.
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Lin, Nien-Hung, and 林念鴻. "Abnormal Event Detection Using Bayesian Networks at a Smart Home." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/97r9t4.

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碩士<br>國立中正大學<br>資訊工程研究所<br>103<br>Existing methods have addressed the issue of detecting abnormal events at a smart home for medical care or security monitoring services extensively in the past decades. However, most of approaches use wearable sensors that require users to be equipped with the sensor devices at every moment. If the monitored users stop or pause the sensors, any abnormal events are not able to be detected. The use of non-wearable and non-intrusive sensors (e.g., IP cameras) is necessary for providing better user experiences and achieving sustainable and reliable detection model. However, it is still very challenging to analyze such non-wearable sensor data with a high accuracy. In this work, we propose an event detection model using a Bayesian Network. We first obtain the features by analyzing the daily videos and audios captured from different angles by multiple IP cameras at a smart home. These features are then used to construct a Bayesian network. We propose a probabilistic graph model where the dependence relations are defined in the graph as opposed to the naive Bayesian network. The experiments are presented to demonstrate the performance and utility of our model.
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