Vehicle re-identification based on keypoint segmentation of original image

Hu, Zhijun; Xu, Yong; Raja, S. P.; Cheng, Xianjing; Sun, Lilei; Lian, Wei

doi:10.1007/s10489-022-03192-1

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…Vehicle ReID [13,14] has extensive research and important applications in computer vision. With the rapid development of deep learning techniques, using neural networks has become mainstream for vehicle ReID.…”

Section: Vehicle Re-identification (Reid)mentioning

confidence: 99%

Learning More in Vehicle Re-Identification: Joint Local Blur Transformation and Adversarial Network Optimization

Chen

Sheng

et al. 2022

Applied Sciences

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Vehicle re-identification (ReID) tasks are an important part of smart cities and are widely used in public security. It is extremely challenging because vehicles with different identities are generated from a uniform pipeline and cannot be distinguished based only on the subtle differences in their characteristics. To enhance the network’s ability to handle the diversity of samples in order to adapt to the changing external environment, we propose a novel data augmentation method to improve its performance. Our deep learning framework mainly consists of a local blur transformation and a transformation adversarial module. In particular, we first use a random selection algorithm to find a local region of interest in an image sample. Then, the parameter generator network, a lightweight convolutional neural network, is responsible for generating four weights and then as a basis to form a filter matrix for further blur transformations. Finally, an adversarial module is employed to ensure that as much noise information as possible is added to the image sample while preserving the structure of the training datasets. Furthermore, by updating the parameter generator network, the adversarial module can help produce more appropriate and harder training samples and lead to improving the framework’s performance. Extensive experiments on datasets, i.e., VeRi-776, VehicleID, and VERI-Wild, show that our method is superior to the state-of-the-art methods.

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Section: Vehicle Re-identification (Reid)mentioning

confidence: 99%