Multiple object tracking using artificial intelligence - Ji, Dongsheng (Nanyang Technological University, 2023)
Multiple Object Tracking (MOT) is a key focus in machine vision research. It seeks to follow and label several objects in a video over time, calculating their paths. The challenge is to create a model that works well in tough conditions like occlusions and shadows. Different MOT models based on various principles have been suggested, and while accuracy has improved, there’s still a need to enhance the model’s performance. The dissertation employs BoT-SORT [1] as its baseline. However, BoT-SORT faces challenges in object recognition and re-identification, particularly in sce narios with object overlap, occlusion, and uniform visual attributes [2] [3] [4]. To address this, the dissertation integrates various re-identification (ReID) back bones with the BoT-SORT architecture to assess resulting model performance. Among the three models created, the one with the SBS R50 ReID backbone achieves the highest IDF1 score of 81.9%. To enable visual observation and fa cilitate comparison of tracker performance, tracking samples are executed under diverse video conditions. The dissertation includes a detailed analysis of perfor mance results and a discussion of future research directions for MOT. Keywords: Multiple Object Tracking, BoT-SORT, Re-identification.