本文爲澳大利亞昆士蘭理工大學（作者：Rohit Ramesh）的碩士論文，共93頁。
目標檢測與跟蹤是監視系統領域的重要組成部分。室外監控系統主要利用可視攝像機實現各種應用，如行人檢測、人臉識別和人體姿勢估計。在城市街道、公共汽車站、地鐵和機場內和周圍安裝多個攝像頭的目的是監測一個人或一羣人所遭受的各種威脅或危險，並立即採取行動以避免或應對這些威脅或危險。比如在擁擠的公共場所丟失了揹包，可以很容易地根據視頻尋找到感興趣的不尋常事件，並可能提供對當地環境潛在威脅的視覺線索。
對於計算機視覺領域存在的異常，可以從兩個不同的角度來觀察異常事件檢測器。其中一種可能是實時威脅，如丟包；另一種可能是不可預測的行爲，如滑冰、騎自行車、跑步和追逐，與其他正常的人羣行爲（如走路）相比，這些行爲也被視爲不尋常的事件檢測。這個碩士學位項目的目標是通過使用可靠的描述性圖像特徵，利用最先進的深度學習方法，將本文工作與現有的手工提取特徵進行比較，研究異常事件檢測。爲此，選擇了C3D（3D卷積神經網絡）從基線模型中提取特徵。整個過程包括訓練模型、從C3D中提取特徵、在MATLAB中進行各種預處理，達到基於幀的檢測和基於塊的檢測。一個在全球範圍內廣泛應用的異常檢測數據集UCSD被用來進行實驗。
本碩士論文的主要貢獻是利用最先進的C3D深度學習網絡，開發基於幀和基於塊的異常事件檢測方法。首先，將UCSD數據集上的三個正交平面線性二值模式（LBPTOP）異常事件檢測作爲特徵提取的基線系統。接着，將三維卷積神經網絡（C3D）應用到同一基線模型中，並觀察其特徵的差異。實驗結果顯示了深度學習方法的強大潛力，它可以檢測通過視頻反饋觀察到的人羣中的異常情況。
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.
- 引言
- 文獻回顧
- 視頻特徵的空時表達形式
- 異常檢測
- 結論與未來工作展望
附錄A 數據蒐集
附錄B 校正
附錄C 遮擋與照明效果
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