Precise Localization Strategy for Reliable Multi-Vehicle Tracking in Unregulated Road Traffic Based on Enhanced Detection Transformer

Abstract

Objectives: To develop a reliable multi-vehicle detection-based tracking framework under unregulated traffic scenario using enhanced vision transformer model that can precisely localize and track the vehicles. Method: The proposed framework involves a three-level enhancements, (1) Adaptive frame slicing technique in the input data loader to capture the discriminative features of near and far away vehicles in the video frame (2) Geometric positional encoding to provide clear spatial cues and fusion of features, allowing the model to better separate and detect multi-scale vehicles (3) Dual inferencing of sliced and full frame with DIoU-NMS post-processing technique to remove redundant detections. The proposed framework is evaluated on our custom developed Active Learning based vehicle dataset, “AU-INV-P-PALS”, and two public traffic datasets- “IITM-HeTra and Udacity-Fixed small”. Findings: In comparison with the existing baseline DETR model, the proposed model with SGD optimization showed 9.2% increase in the average precision for IoU = 0.5:0.95 indicating its precise localization ability. The model localizes vehicles that occupy as little as 0.2% of the surveillance video frame area. The observed tracking accuracy on real-time traffic CCTV videos indicates that the model can adapt to unregulated traffic environment and achieved appreciable score of about 85.4%, maintaining the tracking stability. The average F1-score of the enhanced DETR obtained for the test frames indicate the generalization ability of the model across different frames. Novelty: The proposed work introduces adaptive frame slicing scheme in the input data loader and geometric positional encoding which enables the detection of faraway vehicles with high accuracy in wide area surveillance imagery. Further, the enhanced model is trained using active learning based custom developed vehicle dataset that significantly reduces the reliance on massive datasets. Keywords: Deep Learning, Active Learning, Unregulated Road Traffic Control, Vision Transformer