本文選題：交通狀態(tài)識別 + 交通圖像�。� 參考：《西安電子科技大學(xué)學(xué)報》2017年06期

【摘要】：對交通狀態(tài)進行準(zhǔn)確識別可以主動預(yù)警將要進入本路段的駕駛員避開擁堵,以免加重?fù)矶鲁潭?同時也是科學(xué)制定主動交通管理決策的基礎(chǔ),有利于及時疏導(dǎo)擁堵,提高道路運行效率,節(jié)能減排.首先從交通監(jiān)控視頻中采集圖像,標(biāo)注道路為興趣區(qū),并對道路圖像做角度和尺度的歸一化處理;然后提取興趣區(qū)圖像的平均梯度、角點個數(shù)和長邊緣比例3個特征;最后,利用模糊C均值聚類算法將圖片所呈現(xiàn)的交通狀態(tài)分為暢通和擁堵兩種狀態(tài).實驗結(jié)果表明,文中算法可以有效識別圖像中的交通狀態(tài),正確率達(dá)到了94%以上,而且較基于視頻的交通狀態(tài)識別方法,該方法也大大降低了實現(xiàn)成本.
[Abstract]:Accurate identification of traffic conditions can actively warn drivers who will enter this section of the road to avoid congestion, so as to avoid aggravating congestion, and it is also the basis of scientific decision-making on active traffic management, which is conducive to timely dredging congestion. Improve road operation efficiency, energy conservation and emission reduction. Firstly, the images are collected from the traffic surveillance video, the road is marked as the area of interest, and the road image is normalized in angle and scale. Then, the average gradient, the number of corner points and the proportion of long edges are extracted. Finally, the average gradient of the image, the number of corner points and the proportion of long edges are extracted. A fuzzy C-means clustering algorithm is used to classify the traffic state presented by the image into two states: unblocked and congested. The experimental results show that the proposed algorithm can effectively recognize the traffic state in the image, and the correct rate is over 94%, which is much lower than that of the video-based traffic state recognition method.
【作者單位】： 長安大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61572083) 陜西省自然科學(xué)基金資助項目(2015JQ6230) 中央高校基本科研業(yè)務(wù)費專項資金資助項目(310824152009)
【分類號】：TP391.41
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本文編號：2118345