A novel approach for reliable pedestrian trajectory collection with behavior-based trajectory reconstruction for urban surveillance systems

Collecting reliable pedestrian trajectories in pedestrian behavior analysis, trajectories broken by frame sampling and trajectories crossing in multi-object conditions often hinder their performance of existing pedestrian tracking models. Despite attempts to address these issues by performing detection and tracking simultaneously using deep learning algorithms, previous methods still struggle with errors such as mistaking a single pedestrian for multiple pedestrians. We propose a novel approach to efficiently collect and correct pedestrian trajectories with minimized practical errors in multi-object conditions for urban surveillance systems. Our system utilizes a single vision sensor to automatically collects trajectories of multiple pedestrians and employ simple, low-computational algorithms, particularly the Deep simple online real-time tracking (Deep SORT) method, to calibrate the trajectories from tracking-by-detection models. Additionally, our system identifies and merges broken pedestrian trajectories, treating them as potential single trajectories, while considering their spatiotemporal ranges. We evaluate the proposed system by implementing it on real testbed video footage. Our method significantly improves practical errors and achieves more accurate pedestrian trajectories compared to existing models, and exhibits robust characteristics, effectively handling complex situations such as occlusions and crowds.