本文為美國喬治亞理工學(xué)院（作者：Keitaro Kamiya）的碩士論文，共136頁。

本文提出了一個車輛檢測跟蹤監(jiān)控系統(tǒng)的框架。給出了一個優(yōu)化的目標(biāo)檢測模板，并考慮了該方法在車輛計數(shù)應(yīng)用中的可行性和有效性，實現(xiàn)了基于各路段交通狀態(tài)速度變化的虛假檢測過濾操作和閉塞處理，考慮了跟蹤子空間異常仿射變換及其高波動平均加速度數(shù)據(jù)的技術(shù)。結(jié)果表明，考慮到真實檢測率和虛假檢測率之間的權(quán)衡關(guān)系，該方法具有較好的綜合性能。過濾操作在去除大多數(shù)非車輛一樣移動的物體方面取得了顯著成功。采用的閉塞處理技術(shù)也提高了系統(tǒng)性能，從而避免了原本會丟失的計數(shù)。對于所有測試的視頻樣本，該框架獲得了較高的正確計數(shù)率（>93%的正確計數(shù)率），同時最小化了錯誤計數(shù)率。在未來的研究中，作者建議針對特定的條件集使用更復(fù)雜的過濾器，以及實現(xiàn)用于檢測不同阻塞情況的識別分類器。

This thesis presents a framework for motorvehicle detection-tracking surveillance systems. Given an optimized objectdetection template, the feasibility and effectiveness of the methodology isconsidered for vehicle counting applications, implementing both a filteringoperation of false detection, based on the speed variability in each segment oftraffic state, and an occlusion handling technique which considers the unusualaffine transformation of tracking subspace, as well as its highly fluctuatingaveraged acceleration data. The result presents the overall performanceconsidering the trade-off relationship between true detection rate and falsedetection rate. The filtering operation achieved significant success inremoving the majority of non-vehicle elements that do not move like a vehicle.The occlusion handling technique employed also improved the systemsperformance, contributing counts that would otherwise be lost. For all videosamples tested, the proposed framework obtained high correct count (>93%correct counting rate) while simultaneously minimizing the false count rate.For future research, the author recommends the use of more sophisticatedfilters for specific sets of conditions as well as the implementation ofdiscriminative classifier for detecting different occlusion cases.

引言

研究目標(biāo)

文獻回顧

本文所提出框架的文獻回顧

HAAR級聯(lián)優(yōu)化

車輛計數(shù)方法

測試、結(jié)果與討論

結(jié)論與建議
附錄A 用于校正方法的代碼
附錄B 所有樣本的逐車道結(jié)果
附錄C 所有樣本的參考速度與最小速度

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