Summary of Real-time Ai-driven People Tracking and Counting Using Overhead Cameras, by Ishrath Ahamed et al.
Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras
by Ishrath Ahamed, Chamith Dilshan Ranathunga, Dinuka Sandun Udayantha, Benny Kai Kiat Ng, Chau Yuen
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to accurate people counting in smart buildings and intelligent transportation systems. The authors combine a new object tracking algorithm, a novel counting algorithm, and a fine-tuned object detection model to achieve high accuracy in real-time people counting. Their method is capable of achieving 97% accuracy with a frame rate of 20-27 FPS on a low-power edge computer. This is particularly important for emergency situations where accurate occupant counts are vital for safe evacuation. The proposed approach addresses the limitations of existing methods, which struggle to maintain accuracy in large crowds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves an important problem: accurately counting people in smart buildings and transportation systems. Right now, methods can be too inaccurate when there are many people around. To fix this, the researchers created a new way to track objects, count people, and detect objects using a special model. This method is very good at counting people in real-time on low-power computers. It’s especially important for emergencies where it’s crucial to know how many people need to be evacuated safely. The researchers’ approach can help make smart buildings and transportation systems safer and more efficient. |
Keywords
» Artificial intelligence » Object detection » Object tracking
