Real-time vehicle orientation classification and viewpoint-aware vehicle re-identification
| Autor: | Oliver Tamas Kocsis, Tunc Alkanat, Egor Bondarev, Peter H.N. de With |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
rechtvaardigheid en sterke instellingen Vehicle tracking system SDG 16 - Peace Computer science Orientation (computer vision) business.industry SDG 16 – Vrede Deep learning Scene understanding Inference Machine learning computer.software_genre Justice and Strong Institutions Vehicle re-identification Robustness (computer science) Algorithmic efficiency Benchmark (computing) Artificial intelligence business Image retrieval computer CNN |
| Zdroj: | Image Processing: Algorithms and Systems |
| ISSN: | 2470-1173 |
| Popis: | Vehicle re-identification (re-ID) is based on identity matching of vehicles across non-overlapping camera views. Recently, the research on vehicle re-ID attracts increased attention, mainly due to its prominent industrial applications, such as post-crime analysis, traffic flow analysis, and wide-area vehicle tracking. However, despite the increased interest, the problem remains to be challenging. One of the most significant difficulties of vehicle re-ID is the large viewpoint variations due to non-standardized camera placements. In this study, to improve re-ID robustness against viewpoint variations while preserving algorithm efficiency, we exploit the use of vehicle orientation information. First, we analyze and benchmark various deep learning architectures in terms of performance, memory use, and cost on applicability to orientation classification. Secondly, the extracted orientation information is utilized to improve the vehicle re-ID task. For this, we propose a viewpoint-aware multi-branch network that improves the vehicle re-ID performance without increasing the forward inference time. Third, we introduce a viewpoint-aware mini-batching approach which yields improved training and higher re-ID performance. The experiments show an increase of 4.0% mAP and 4.4% rank-1 score on the popular VeRi dataset with the proposed mini-batching strategy, and overall, an increase of 2.2% mAP and 3.8% rank-1 score compared to the ResNet-50 baseline. |
| Databáze: | OpenAIRE |
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