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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.10072 |
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| _version_ | 1866913578776264704 |
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| author | Ahamed, Ishrath Ranathunga, Chamith Dilshan Udayantha, Dinuka Sandun Ng, Benny Kai Kiat Yuen, Chau |
| author_facet | Ahamed, Ishrath Ranathunga, Chamith Dilshan Udayantha, Dinuka Sandun Ng, Benny Kai Kiat Yuen, Chau |
| contents | Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often losing accuracy with even a few additional people. To address this limitation, this study proposes a novel approach combining a new object tracking algorithm, a novel counting algorithm, and a fine-tuned object detection model. This method achieves 97% accuracy in real-time people counting with a frame rate of 20-27 FPS on a low-power edge computer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras Ahamed, Ishrath Ranathunga, Chamith Dilshan Udayantha, Dinuka Sandun Ng, Benny Kai Kiat Yuen, Chau Computer Vision and Pattern Recognition Artificial Intelligence Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often losing accuracy with even a few additional people. To address this limitation, this study proposes a novel approach combining a new object tracking algorithm, a novel counting algorithm, and a fine-tuned object detection model. This method achieves 97% accuracy in real-time people counting with a frame rate of 20-27 FPS on a low-power edge computer. |
| title | Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.10072 |