Night-Time Vehicle Distance Estimation Using Camera Geometry and Deep Learning
| Autor: | Trong-Hop Do, Minh-Quan Pham, Nhu-Ngoc Dao, Quang-Dung Pham, Dang-Khoa Tran, Dinh-Quang Hoang, Chunghyun Lee, Sungrae Cho |
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| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
business.industry Computer science Deep learning 010401 analytical chemistry 05 social sciences Base (geometry) Mobile computing Geometry Cloud computing Video processing 01 natural sciences 0104 chemical sciences Simplicity (photography) 0502 economics and business Resource allocation Artificial intelligence Duration (project management) business |
| Zdroj: | ICOIN |
| Popis: | Vehicle distance estimation has been considered as one of the important topics in traffic video processing. There have been many algorithms proposed to deal with this problem. However, these algorithms all have their own drawbacks such as long processing duration, huge data requirements, extremely computational consumption, and low accuracy response. In this circumstance, in this paper, a novel algorithm based on a combination of deep learning and camera geometry is proposed for vehicle distance estimation. The proposed algorithm demonstrates fast processing duration which is contributed by the deep learning base technique. Moreover, thanks to the simplicity of camera geometry, the proposed algorithm requires minimal data to perform the estimation. By combining deep learning and camera geometry, the proposed algorithm can provide high estimation accuracy while keeping the model simple and fast. The performance of the proposed algorithm is verified through experiments results. |
| Databáze: | OpenAIRE |
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