10.64886/oajea.0102.006

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Abstract: Road traffic accidents and overspeeding remain critical public safety challenges worldwide, disproportionately affecting rapidly urbanizing regions with limited automated enforcement capacity. This paper presents an IoT-Enabled Speed and Accident Detection Platform that incorporates multi-object tracking and deep-learning based object recognition, and calibration-based speed estimation into a unified, real-time intelligent traffic monitoring framework. The proposed system employs YOLOv8 for accurate, high-speed vehicle detection; ByteTrack for consistent persistent identity assignment across consecutive video frames; and a pixel-to-real-world-distance calibration method for precise vehicle speed computation. The platform automatically classifies vehicle types, identifies overspeeding behavior against configurable speed thresholds, generates real-time visual alerts, counts traffic volume through a configurable virtual line-crossing mechanism, and supports future IoT and cloud-platform integration for accident detection, remote monitoring, and emergency response automation. Experimental evaluation on traffic video sequences recorded at an urban intersection in Dhaka, Bangladesh, shows a mean Average Precision (mAP@0.5) as 0.82, a Multi-Object Tracking Accuracy or MOTA of 0.75, an IDF1 of 0.79, and a speed estimation Mean Absolute Error or MAE as 3.2 km/h. The reconfigurable, scalable architecture positions this platform as a practical and cost-effective foundation for intelligent transportation systems (ITS) and smart city infrastructure.