MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916617642835968 |
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| author | Vaghela, Arpitsinh Lu, Duo Verma, Aayush Atul Chakravarthi, Bharatesh Wei, Hua Yang, Yezhou |
| author_facet | Vaghela, Arpitsinh Lu, Duo Verma, Aayush Atul Chakravarthi, Bharatesh Wei, Hua Yang, Yezhou |
| contents | Single camera 3D perception for traffic monitoring faces significant challenges due to occlusion and limited field of view. Moreover, fusing information from multiple cameras at the image feature level is difficult because of different view angles. Further, the necessity for practical implementation and compatibility with existing traffic infrastructure compounds these challenges. To address these issues, this paper introduces a novel Bird's-Eye-View road occupancy detection framework that leverages multiple roadside cameras to overcome the aforementioned limitations. To facilitate the framework's development and evaluation, a synthetic dataset featuring diverse scenes and varying camera configurations is generated using the CARLA simulator. A late fusion and three early fusion methods were implemented within the proposed framework, with performance further enhanced by integrating backgrounds. Extensive evaluations were conducted to analyze the impact of multi-camera inputs and varying BEV occupancy map sizes on model performance. Additionally, a real-world data collection pipeline was developed to assess the model's ability to generalize to real-world environments. The sim-to-real capabilities of the model were evaluated using zero-shot and few-shot fine-tuning, demonstrating its potential for practical application. This research aims to advance perception systems in traffic monitoring, contributing to improved traffic management, operational efficiency, and road safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring Vaghela, Arpitsinh Lu, Duo Verma, Aayush Atul Chakravarthi, Bharatesh Wei, Hua Yang, Yezhou Computer Vision and Pattern Recognition Single camera 3D perception for traffic monitoring faces significant challenges due to occlusion and limited field of view. Moreover, fusing information from multiple cameras at the image feature level is difficult because of different view angles. Further, the necessity for practical implementation and compatibility with existing traffic infrastructure compounds these challenges. To address these issues, this paper introduces a novel Bird's-Eye-View road occupancy detection framework that leverages multiple roadside cameras to overcome the aforementioned limitations. To facilitate the framework's development and evaluation, a synthetic dataset featuring diverse scenes and varying camera configurations is generated using the CARLA simulator. A late fusion and three early fusion methods were implemented within the proposed framework, with performance further enhanced by integrating backgrounds. Extensive evaluations were conducted to analyze the impact of multi-camera inputs and varying BEV occupancy map sizes on model performance. Additionally, a real-world data collection pipeline was developed to assess the model's ability to generalize to real-world environments. The sim-to-real capabilities of the model were evaluated using zero-shot and few-shot fine-tuning, demonstrating its potential for practical application. This research aims to advance perception systems in traffic monitoring, contributing to improved traffic management, operational efficiency, and road safety. |
| title | MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.11287 |