Model Pelacakan Objek Berbasis YOLO-OCSort Untuk Estimasi Kepadatan Lalu Lintas - Linda, Deppi; Agus, Isnandar; N (Institut Informatika Dan Bisnis Darmajaya, 2025)
Accurate traffic density estimation is a crucial component in intelligent transportation systems for optimizing urban trafficmanagement. This research develops an object tracking model based on YOLOv8 and Online Clustering SORT (OCSort) integration for realtime trafficdensity estimation. YOLOv8 is used as a highaccuracy object detector, while OCSort implements a tracking algorithm with observationcentric association approach to overcome conventional tracking limitations in handling nonlinear motion and temporal occlusion. Comprehensive evaluation was conducted using MOT17FRCNN benchmark Dataset for tracking performance measurement and UADETRAC Dataset subset for computational performance analysis. The system was tested on Intel Core i7 Generation 13 hardware, 32 GB RAM, and NVIDIA GeForce RTX 3060 12 GB VRAM. Evaluation results show MOTA of 0.4910, MOTP 0.9072, and IDF1 0.6702 with very low identity switches (7 occurrences) and 65 fragmentations. Comparison with stateoftheart methods using 2001 frames demonstrates significant YOLOOCSort superiority in computational efficiency with best processing time of 118.01 seconds, outperforming ByteTrack (146.98s), BoTSORT (161.91s), and DeepSORT (152.33s). The system achieves endtoend FPS of 17.0, model processing time 33.8 ms per frame, and detects average 20.1 objects per frame with confidence score 0.608. Statistical analysis with 95% confidence interval confirms consistency and reliability of evaluation results. The contribution of this research is empirical validation of YOLOv8OCSort integration effectiveness that produces optimal balance between tracking accuracy and computational efficiency for realtime traffic cmonitoring applications.