基于区域与全局融合特征的以图搜车算法

A Vehicle Retrieval Algorithm Based on Regional and Global Fusion Feature

在视频监控场景中,由于车辆自身外观的多样性和相似性以及无约束的监控环境,以致很难通过全局外观特征区分不同的车辆目标。与全局外观特征相比较,局部区域特征更具区分能力。同时,为了兼顾算法的速度,本文提出一种基于区域与全局融合特征的以图搜车算法。该算法分为三个阶段:首先,以车辆IDs作为标签信息,训练一个车辆的全局特征网络;其次,加入局部区域特征网络,进而联合训练局部区域特征与全局特征网络;在推理阶段,仅采用全局特征网络的特征计算车辆图像之间的相似度。本文采用视频监控场景的图片作为数据集进行算法测试,结果显示所提出的方法的Top10性能达到了91.3%,特征提取时间与单次特征比对时间分别为13.8ms和0.0016ms,满足了应用需求。

In video surveillance scenario,due to the diversity and similarity of vehicle appearance and unconstrained surveillance environment,it is difficult to distinguish different vehicles by global appearance features.Compared with global appearance features,local region features are more distinctive for vehicle retrieval.At the same time,in order to balance the speed of the algorithm,a vehicle retrieval algorithm based on regional and global fusion feature is proposed in this paper.The algorithm is divided into three stages:firstly,using vehicle IDs as the label to train a vehicle’s global feature network;secondly,adding a local region feature network,and then the local region feature network and the global feature network are jointly trained;in the inference stage,only using global feature network’s features to calculate the similarity between different vehicle images.In this paper,the images of the surveillance video scenario are used as the data set to test the algorithm.The results showed that the performance of Top10 reached 91.3%,and the time of feature extraction and single feature comparison were 13.8 ms and 0.0016 ms respectively.Therefore,satisfied the application demand.