Multi-block fusion for vehicle re-identification
Multi-block fusion for vehicle re-identification
- Author(s): Yunping Zhang and K. Mikolajczyk
- DOI: 10.1049/cp.2019.1165
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- Author(s): Yunping Zhang and K. Mikolajczyk Source: 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019), 2019 p. 7 (38 – 43)
- Conference: 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019)
- DOI: 10.1049/cp.2019.1165
- ISBN: 978-1-83953-109-5
- Location: London, UK
- Conference date: 16-18 Dec. 2019
- Format: PDF
The extensive coverage of surveillance camera networks has supported the ever-growing research of vehicle re-identification (re-ID) due to their significant applications in matching and tracking vehicles-of-interest. The inherent challenging characteristics such as intra-class variance and inter-class similarity make the re-identification one of the most difficult tasks in computer vision. In this paper, we proposed a novel approach for vehicle re-id based on multi-block features. It implements the idea of information fusion from intermediate levels of representation and multi-stage supervision into a fully convolutional neural network. To demonstrate the effectiveness and superiority of our approach we perform extensive experiments and analysis on two standard vehicle re-id benchmarks.
Inspec keywords: image fusion; computer vision; convolutional neural nets; cameras
Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Neural computing techniques
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