Learning More in Vehicle Re-Identification: Joint Local Blur Transformation and Adversarial Network Optimization
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Digital Media Forensic Detection · Advanced Image Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Beihang Hangzhou Innovation Institute Yuhang, Xixi Octagon City, Yuhang District, Hangzhou 310023, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China · Macao Polytechnic University · State Key Laboratory of Software Development Environment
References (61)
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