General Information

根據車輛重識別中區域置信度不同,提出了基於高置信局部特徵的車輛重識別優化演算法。首先,利用車輛關鍵點檢測獲得對應的多個關鍵點座標資訊,分割出車標擴散區域和其他重要的局部區域。根據車標擴散區域的高區分度特性,提升局部區域的置信度。使用多層卷積神經網路對輸入圖片進行處理,根據局部區域分割資訊,對卷積得到的特徵張量進行空間維度上的切割,獲得代表全域資訊和關鍵局部資訊的特徵張量。然後,通過全連接層特徵張量轉化為表示車輛個體的一維向量,計算損失函數。最後,在測試階段使用全域特徵,並利用訓練好的車標擴散區域提取分支獲得高置信局部特徵,縮短局部識別一致的車輛目標距離。在典型車輛重識別資料集VehicleID上進行測試,驗證了所提演算法的有效性。

 

In solving vehicle re-identification problems, different vehicle regions have different recognition degree of confidence. Based on this observation, we propose a vehicle re-identification optimization algorithm that takes advantage of the high-confidence local features. First, the vehicle key point detection algorithm is utilized to obtain the corresponding multiple key points' coordinate information of the vehicles, and to divide the vehicle brand extension regions and other prominent local regions. As the brand extension region is the most salient region, we propose to improve the degree of confidence of the local region in the testing phase. We also utilize a multi-layer convolutional neural network for processing the input images, cutting the convolutional features into several parts based on the obtained local regions, and acquiring feature tensors representing global and key regional information. Then, a fully connected layer is applied to combine the above features and output a one-dimensional vector for loss function calculating. In the testing phase, to reduce the target distances of vehicles with the same local identification, we propose to utilize the global features together with the high-confidence local features obtained by trained brand extension region extraction branch. Experiments on the widely used vehicle re-identification VehicleID dataset show that the proposed algorithm is effective.