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A large number of surveillance cameras deployed in highways, intersections and other areas can only provide a specific angle and a small range of vehicle images. Driven by deep learning technology, more and more researchers have started to shift towards the deep convolutional neural network, which solves the previous problem of insufficient feature extraction expression using traditional methods.Įxisting vehicle Re-ID work 1, 2, 3, 4, 5, 6 is mainly through road surveillance video to obtain vehicle data.

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The use of vehicle Re-ID algorithm can automatically perform the work of image matching, solving the problem of vehicle identification due to the influence of external conditions, such as artificially blocked license plates, obstacle blocking, blurred images, etc., saving manpower and consuming less time, providing strong technical support for the construction and maintenance of urban security order and guaranteeing public safety.

Similar content being viewed by othersĪs an important component of intelligent transportation systems, vehicle re-identification (Re-ID) aims to find the same vehicle from the vehicle images taken by different surveillance cameras. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. It plays a crucial role in the development of safe cities and smart cities. Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image.

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