Computer vision has advanced research methodologies, enhancing system services across various fields. It is a core component in traffic monitoring systems for improving road safety; however, these monitoring systems don't preserve the privacy of pedestrians who appear in the videos, potentially revealing their identities. Addressing this issue, our paper introduces Video-to-Text Pedestrian Monitoring (VTPM), which monitors pedestrian movements at intersections and generates real-time textual reports, including traffic signal and weather information. VTPM uses computer vision models for pedestrian detection and tracking, achieving a latency of 0.05 seconds per video frame. Additionally, it detects crossing violations with 90.2% accuracy by incorporating traffic signal data. The proposed framework is equipped with Phi-3 mini-4k to generate real-time textual reports of pedestrian activity while stating safety concerns like crossing violations, conflicts, and the impact of weather on their behavior with latency of 0.33 seconds. To enhance comprehensive analysis of the generated textual reports, Phi-3 medium is fine-tuned for historical analysis of these generated textual reports. This fine-tuning enables more reliable analysis about the pedestrian safety at intersections, effectively detecting patterns and safety critical events. The proposed VTPM offers a more efficient alternative to video footage by using textual reports reducing memory usage, saving up to 253 million percent, eliminating privacy issues, and enabling comprehensive interactive historical analysis.

The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being. This paper proposes a novel framework utilizing these technologies for enhanced 3D object detection and activity classification in urban traffic scenarios. By employing elevated LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian activity monitoring. To overcome urban data scarcity, we create a specialized dataset through simulated traffic environments in Blender, facilitating targeted model training. Our approach employs a modified Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) for robust 3D detection and PointNet for classifying pedestrian activities, significantly benefiting urban traffic management and public health by offering insights into pedestrian behavior and promoting safer urban environments. Our dual-model approach not only enhances urban traffic management but also contributes significantly to public health by providing insights into pedestrian behavior and promoting safer urban environment.

Cities across the globe are undergoing makeovers - swapping out old, antiquated technology for new, sleek alternatives. The development and implementation of computer vision and real-time analytics are ushering in the newest wave of smart cities. The combination of cloud-based dashboards and machine learning are providing actionable data to be collected and understood regarding everything from vehicle concentration to pedestrian activity. As cities continue to push forward and develop socially and technologically, there is no doubt we will continue to see cities incorporate tools like Artificial Intelligence (AI) to facilitate such changes. Despite the fact that eye-popping technologies like drones and robots are at the forefront of this technological revolution, there are also a number of unexpected ways cities are becoming smarter.

Trying to figure out what a vehicle or pedestrian is about to do is tough enough for human drivers. But it's something that the AI systems that end up in autonomous vehicles will have to figure out. Luminar and Volvo announced that they're closer to figuring that out using high-resolution LiDAR. Volvo announced back in June that it would be partnering with LiDAR company, Luminar. At the LA Auto Show, they announced that they would be using the high-resolution long-range sensor to figure out what the intentions of pedestrians.