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Vehicle model recognition is a typical fine-grained classification task that has a wide range of application prospects in safe cities and constitutes a research hotspot in the field of computer vision. Vehicles in images can appear at various angles, resulting in large differences in appearance. The existence of “multiviews” renders vehicle model recognition challenging. Recent research on vehicle model recognition has not fully explored the pose information of vehicles in different images, resulting in low model performance. In this study, we use vehicle pose information to solve the multiview vehicle model recognition (MV-VMR) problem and design a convolutional neural network (CNN) model with embedded vehicle pose information, known as the embedding pose CNN (EP-CNN). The proposed model includes two subnetworks: the pose estimation subnetwork (PE-SubNet) and vehicle model classification subnetwork (VMC-SubNet). PE-SubNet extracts the vehicle pose information, including the pose features and vehicle viewpoint. In VMC-SubNet, considering the scale variation of vehicles, an improved squeeze-and-excitation (SE) block, named the MultiSE block is implemented. We embed the vehicle viewpoint into the MultiSE block, which reweighs each channel such that the extracted features elicit different responses to different viewpoints. Subsequently, the pose features and classification features are integrated for classification. Experiments are conducted on the benchmark CompCars web-nature and Stanford Cars datasets. The results demonstrate that the proposed EP-CNN method can achieve higher recognition accuracy than most classic CNN models and several state-of-the-art fine-grained vehicle model classification algorithms. Code has been made available at: https://github.com/HFUT-CV/EP-CNN. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation, automated traffic surveillance is considered as an important component. In the field of traffic surveillance, it is particularly important to achieve robust and real-time tracking of vehicles in complex scenes. In this paper, a robust real-time vehicle tracking model named NoisyOTNet is proposed, which formulates tracking as reinforcement learning with parameter space noise. In this formulation, the exploration ability of the model is enhanced to improve the robustness of tracking. Specifically, we develop a new implementation for noisy network based on deep deterministic policy gradients (DDPGs) with parameter noise, which can better cope with the tracking task and directly predict the tracking result. To improve the tracking accuracy in complex conditions, e.g. fast motion and large deformation, this paper presents an adaptive update strategy that can exploit the vehicle spatial-temporal information based on Upper Confidence Bound (UCB) algorithm by exploiting. Moreover, as for the recovery of the lost target, a relocation algorithm based on incremental learning is developed. The results of extensive experiments demonstrate that the proposed NoisyOTNet can effectively track vehicles in complex scenes and achieve competitive performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Vehicle Re-Identification (Re-ID) is a challenging vision task mainly because the appearance of a vehicle varies dramatically under different viewpoints. Moreover, different vehicles with the same model and color commonly show similar appearance, thus are hard to be distinguished. To alleviate negative effects of viewpoint variance, we design a multi-view branch network where each branch learns a viewpoint-specific feature without parameter sharing. Being able to focus on a limited range of viewpoints, this viewpoint-specific feature performs substantially better than the general feature learned by an uniform network. To further differentiate visually similar vehicles, we strengthen the discriminative power on their subtle local differences by introducing a spatial attention model into each feature learning branch. The multi-view feature learning and spatial attention learning compose our neural network architecture, which is trained end to end with the softmax loss and triplet loss, respectively. We evaluate our methods on two large vehicle Re-ID datasets, i.e., VehicleID and VeRi-776, respectively. Extensive experiments show that our methods achieve promising performance. For example, we achieve mAP accuracy of 76.78% and 72.53% on VehicleID and VeRi-776 dataset respectively, substantially better than current state-of-the art. [ABSTRACT FROM AUTHOR]