本文選題：車輛品牌型號識別　切入點：方向梯度直方圖　出處：《計算機工程》2017年05期 　論文類型：期刊論文

【摘要】：針對傳統(tǒng)車輛識別算法魯棒性及實時性不強的問題,結(jié)合局部線性約束編碼(LLC)和加權(quán)空間金字塔匹配(SPM)模型,提出一種車輛品牌型號精細識別算法。提取圖像方向梯度直方圖特征,通過LLC對圖像特征進行編碼映射,得到具有語義信息的圖像表達向量,以提高識別的準確率。利用加權(quán)SPM模型將空間位置信息引入圖像表達向量中,并將每個圖像的最終表達送入線性支持向量機分類器進行訓(xùn)練與識別。使用交通監(jiān)控攝像頭在不同天氣和光照條件下采集150種車輛類型共56 827張圖像進行實驗,結(jié)果表明,該算法可有效改善識別效果,提高識別速度。
[Abstract]:Aiming at the problem that the traditional vehicle recognition algorithm is not robust and real-time, the local linear constraint coding (LLC) and the weighted space pyramid matching (SPM) model are combined. A fine recognition algorithm for vehicle brand model is proposed, which extracts the image direction gradient histogram feature, encodes and maps the image feature through LLC, and obtains the image expression vector with semantic information. In order to improve the accuracy of recognition, the weighted SPM model is used to introduce the spatial position information into the image expression vector. The final expression of each image is sent to the linear support vector machine classifier for training and recognition. The traffic surveillance camera is used to collect a total of 56,827 images of 150 vehicle types under different weather and light conditions. The results show that, The algorithm can effectively improve the recognition effect and speed.
【作者單位】： 中山大學(xué)工學(xué)院智能交通研究中心;廣東省智能交通系統(tǒng)重點實驗室;視頻圖像智能分析與應(yīng)用技術(shù)公安部重點實驗室;
【基金】：國家科技支撐計劃項目(2014BAG01B04)
【分類號】：TP391.41
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本文編號：1619562