Summary of Video-to-text Pedestrian Monitoring (vtpm): Leveraging Computer Vision and Large Language Models For Privacy-preserve Pedestrian Activity Monitoring at Intersections, by Ahmed S. Abdelrahman et al.
Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersections
by Ahmed S. Abdelrahman, Mohamed Abdel-Aty, Dongdong Wang
First submitted to arxiv on: 21 Aug 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 The paper introduces Video-to-Text Pedestrian Monitoring (VTPM), a computer vision-based system that generates real-time textual reports of pedestrian activity at intersections. The system uses models for pedestrian detection and tracking, achieving a latency of 0.05 seconds per video frame. It also detects crossing violations with 90.2% accuracy by incorporating traffic signal data. The proposed framework is equipped to generate textual reports while stating safety concerns like crossing violations, conflicts, and the impact of weather on pedestrian behavior, with a latency of 0.33 seconds. To enhance analysis, Phi-3 medium is fine-tuned for historical analysis of generated textual reports. This enables reliable detection of patterns and safety-critical events, improving pedestrian safety at intersections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VTPM is a system that helps make roads safer by using computer vision to track pedestrians at busy intersections. It takes video footage and turns it into text reports in real-time. The system is really good at detecting when someone is crossing the street against the light or not paying attention, which can be very dangerous. The reports also include information about traffic signals and weather, which helps identify patterns that could lead to accidents. This makes it easier for cities to make their roads safer and more efficient. |
Keywords
» Artificial intelligence » Attention » Tracking
