大工至善|大学至真分享 http://blog.sciencenet.cn/u/lcj2212916



已有 249 次阅读 2020-6-26 17:55 |系统分类:科研笔记|文章来源:转载

本文为澳大利亚昆士兰理工大学(作者:Rohit Ramesh)的硕士论文,共93页。








Object detection and tracking contribute animportant part in the field of surveillance systems. Outdoor surveillancesystems mostly utilise visible cameras for various applications like pedestriandetection, face recognition and human pose estimation. The purpose ofinstalling several cameras in and around city streets, bus stations, metros,and airports is to monitor the kinds of threat or danger committed by a singleperson or a group of people and take immediate actions in order to avoid or torespond to them. One of the activities like dropping off a bag in a crowdedpublic place can easily signify unusual events of interest and may provide avisual clue of potential threats to the local environment. With regard to theexisting abnormalities in the domain of computer vision, unusual eventdetectors can be viewed through two different perspectives. One which couldpossibly be a real time threat like dropping off a bag and another isunpredictable behaviour like skating, cycling, running and chasing which arealso considered as unusual event detections in comparison to the other normalbehaviours of the crowd like walking. The goal of this Master degree project isto investigate abnormal event detections through the use of reliabledescriptive image features by utilising state-of-the-art deep learning methodsand comparing the work with the existing handcrafted features. To do so, theC3D (3D Convolutional Neural Network) has been chosen for extracting featuresfrom a baseline model. The whole process will consist of different stepsranging from training the model, extracting features from the C3D and variouspreprocessing work in MATLAB to reach to the frame-based detection andpatch-based detection. One of the notable datasets which is widely used across theglobe for abnormality detection, the UCSD dataset, is utilised for performingthe experiments. The key contribution in this Master thesis is to utilise astate-of-the-art deep learning network, the C3D network, to develop bothframe-based and patch-based detection results for abnormal event detections. Tobegin with, the unusual event detection in crowded scenes by the Linear BinaryPattern from Three Orthogonal Planes (LBPTOP) method on the UCSD dataset isconsidered as a baseline system for the feature extraction. Continuing further,the 3D convolutional neural network (C3D) into the same baseline model has beenimplemented and the differences in the features are observed. The experimentalresults demonstrate the strong potential of a deep learning approach to detectabnormalities in crowds observed through video feeds.


1. 引言

2. 文献回顾

3. 视频特征的空时表达形式

4. 异常检测

5. 结论与未来工作展望








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