| dc.description.abstract | The pedestrian environment in rapidly urbanizing cities such as Dhaka presents acute
safety challenges due to high densities, informal crossings, encroached sidewalks and
limited monitoring infrastructure. This paper presents a comprehensive system that
integrates computer vision (pedestrian detection, multi-object tracking, re-identification
and event detection) with an NLP-powered public-sentiment analysis module, targeted at
pedestrian safety and flow monitoring in Dhaka. A lightweight detector is fine-tuned for
local conditions, a tracker and re-id pipeline supports cross-camera flow analytics, and a
public-sentiment module mines social media and local news to prioritize intervention
zones. The system is designed for edge-server hybrid deployment, emphasizes privacypreserving data handling and produces policy-relevant dashboards (counts, heat-maps,
alerts). Preliminary experiments on urban footage show detection precision of ~85 %,
tracking IDF1 = 72 %, and sentiment analysis accuracy of 78 %, demonstrating viability
for municipal deployment and infrastructure planning within Dhaka city. | en_US |
