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研究生:高義舜
研究生(外文):Yi-Shun Kao
論文名稱:無建背景模式即時物件向量偵測於DSP實作及人行計數之應用
論文名稱(外文):Real-Time Non-Background Motion-Based Object Detection in DSP Implementation and Application for People Counting
指導教授:林道通
指導教授(外文):Daw-Tung,Lin
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:61
中文關鍵詞:視訊監控向量評估三步收尋法移動向量分析最佳化方法三張影像差分法
外文關鍵詞:Video surveillancemotion estimationThree step searchmotion vector analysisoptimizationDM6437DM64463 successive-frames differencing
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隨著安全與隱私的日漸重視,視訊監控的需求也日漸增加。而在監控系統中,最基礎也最重要的是物件偵測技術,尤其是在很複雜繁忙的環境下。本論文提出一種無建背景式即時物件向量偵測演算法,並將所提出的演算法移植至低成本的DSP嵌入式平台,以及將其延伸應用,發展成人行計數系統。此向量偵測方法主要在於利用三步搜尋法來指定方向的向量,並且再透過新的向量分析演算法來分析。若是有物件順著我們指定的方向穿越過我們事先定義的閘門,我們的系統將會偵測出此物件。此外我們也改進與最佳化此向量偵測演算法,然後移植至德州儀器公司的Davinci DM6446和DM DSP EVM。在最後我們也將此方法擴展應用,此方法配合其他像是3 successive-frames differencing等演算法來建構出人行計數系統。在實驗結果中可看出我們所提出的方法能即時運行,而且也具有相當高的偵測準確率。
To ensure public and private safety and security, the demand of intelligent video surveillance has become overwhelmingly increasing. One of the fundamental and important processes of the surveillance system is motion detection especially under very complex situations. The objective of this thesis is to present a non-background motion based object detection algorithm, and to implement the proposed algorithm on a low-cost DSP platform and to extend it to the application of people counting. The proposed motion detection method convolves the estimation of the appointed motion with three step search and motion analysis by a novel motion vector analysis algorithm to detect moving objects passing through a pre-defined gate along the desired direction. We further improve and optimize this motion detection algorithm, and then implement it on TI Davinci DM6446 and DM6437 DSP EVM boards. Besides, in the last part for this thesis shows the extended application of people counting on PC by this algorithm along with 3 successive-frames differencing method and others. The experimental results reveal that the final system works in real-time and performs excellent correct detection accuracy.
Abstract i
Acknowledgements ii
Contents iii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Literature Survey 4
3 Motion Detection with Motion Vector Analysis 8
3.1 Motion Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Motion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 14
4 System Architecture and Implementation 17
4.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Optimization Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18
4.2.1 Compiler Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
4.2.2 Floating Point Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
4.2.3 Loop Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2.4 Intrinsic Functions and Packing Data . . . . . . . . . . . . . . . . . . . . . . . .22
4.2.5 Memory Access Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
4.3 Implementation on TI DM6446 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
5 The Extended Application of People Counting 27
5.1 Object Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.1.1 3-Successive Video Frames Differencing and Fusion . . . . . . . . . . . 28
5.1.2 Hole Filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.1.3 Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.2 Object Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6 Experimental Results 38
6.1 Implementation Results on TI DM6446 . . . . . . . . . . . . . . . . . . . . . . . .38
6.2 Result of People Counting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
7 Conclusion and Future Work 45
Bibliography 46
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