面向交通擁堵的車輛魯棒檢測及分車道到達累計曲線估計
發(fā)布時間：2018-10-21 09:03
【摘要】：針對大交通量擁堵情況下現(xiàn)有視頻車輛檢測技術(shù)不能有效處理車輛相互遮擋而導(dǎo)致的大量漏檢問題,提出了一種面向交通擁堵的車輛魯棒檢測及分車道到達累計曲線估計方法.首先,完成非擁堵區(qū)域的檢測,避免針對交通擁堵停駛車輛進行復(fù)雜遮擋處理及檢測的工作;然后,基于假設(shè)生成和驗證框架,融合Ada Boost分類器與車底陰影檢測結(jié)果,得到車輛魯棒檢測結(jié)果;最后,使用投影畸變車輛穩(wěn)定特征將車輛位置劃歸特定的車道,準確估計分車道車輛到達累計曲線,實現(xiàn)針對交通檢測斷面分車道詳細交通參數(shù)的有效分析.實驗結(jié)果表明:該方法能夠在高峰時段的交通擁堵狀態(tài)下實時進行車輛魯棒檢測并準確地獲取交通參數(shù),有效避免針對車輛遮擋的復(fù)雜處理過程,對解決車輛到達率和車頭時距調(diào)查成本高、工作量大、不確定因素多等問題具有實際的意義.
[Abstract]:In view of the fact that the existing video vehicle detection technology can not effectively deal with a large number of missed detection problems caused by mutual occlusion of vehicles under heavy traffic congestion, a method for robust detection of vehicles and estimation of cumulative curve of lane arrival for traffic jams is proposed in this paper. First of all, the detection of non-congestion areas is completed to avoid complex occlusion processing and detection for traffic jam and stop vehicles. Then, based on the hypothesis generation and verification framework, the Ada Boost classifier and shadow detection results under the vehicle are fused. The vehicle robust detection results are obtained. Finally, the vehicle position is assigned to a specific lane by using the projective distortion vehicle stability feature, and the cumulative curve of vehicle arrival is estimated accurately. To realize the effective analysis of detailed traffic parameters for traffic detection section divided into lanes. The experimental results show that the proposed method can detect the vehicle robust and obtain the traffic parameters accurately in the rush hour traffic congestion, and avoid the complex processing process of vehicle occlusion effectively. It is of practical significance to solve the problems of high cost, heavy workload and many uncertain factors in the investigation of vehicle arrival rate and headway distance.
【作者單位】： 北京工業(yè)大學(xué)城市交通學(xué)院;交通工程北京市重點實驗室(北京工業(yè)大學(xué));北京市城市交通運行保障工程技術(shù)研究中心(北京工業(yè)大學(xué));廊坊師范學(xué)院計算機系;北京工業(yè)大學(xué)建筑工程學(xué)院;北京交通大學(xué)電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61573030,61511130044,61531005) 河北省高等學(xué)�？茖W(xué)技術(shù)研究青年基金資助項目(QN2015209)
【分類號】：TP391.41;U491
，
本文編號：2284635
[Abstract]:In view of the fact that the existing video vehicle detection technology can not effectively deal with a large number of missed detection problems caused by mutual occlusion of vehicles under heavy traffic congestion, a method for robust detection of vehicles and estimation of cumulative curve of lane arrival for traffic jams is proposed in this paper. First of all, the detection of non-congestion areas is completed to avoid complex occlusion processing and detection for traffic jam and stop vehicles. Then, based on the hypothesis generation and verification framework, the Ada Boost classifier and shadow detection results under the vehicle are fused. The vehicle robust detection results are obtained. Finally, the vehicle position is assigned to a specific lane by using the projective distortion vehicle stability feature, and the cumulative curve of vehicle arrival is estimated accurately. To realize the effective analysis of detailed traffic parameters for traffic detection section divided into lanes. The experimental results show that the proposed method can detect the vehicle robust and obtain the traffic parameters accurately in the rush hour traffic congestion, and avoid the complex processing process of vehicle occlusion effectively. It is of practical significance to solve the problems of high cost, heavy workload and many uncertain factors in the investigation of vehicle arrival rate and headway distance.
【作者單位】： 北京工業(yè)大學(xué)城市交通學(xué)院;交通工程北京市重點實驗室(北京工業(yè)大學(xué));北京市城市交通運行保障工程技術(shù)研究中心(北京工業(yè)大學(xué));廊坊師范學(xué)院計算機系;北京工業(yè)大學(xué)建筑工程學(xué)院;北京交通大學(xué)電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61573030,61511130044,61531005) 河北省高等學(xué)�？茖W(xué)技術(shù)研究青年基金資助項目(QN2015209)
【分類號】：TP391.41;U491
，
本文編號：2284635
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