Pedestrian Multiple Object Tracking in Real-Time

Sammanfattning: Multiple object tracking (MOT) is the task of detecting multiple objects in a scene and associating detections over time to form tracks. It is essential for many scene understanding tasks like surveillance, robotics and autonomous driving. Nowadays, the dominating tracking pipeline is to first detect all individual objects in a scene followed by a separate data association step, also known as tracking-by-detection. Recently, methods doing simultaneous detection and tracking has emerged, combining the task of detection and tracking into one single framework. In this project, we analyse performance of multiple object tracking algorithms belonging to both tracking categories. The goal is to examine strengths, weaknesses, and real-time capability of different tracking approaches in order to understand their suitability in different applications. Results show that a tracking-by-detection system with Scaled-YOLOv4 and SORT achieves 46.8% accuracy at over 28 frames per second (FPS) on Nvidia GTX 1080. By reducing the input resolution, inference speed is increased to almost 50 FPS, making it well suitable for real-time application. The addition of a deep re-identification CNN reduces the number of identity switches by 47%. However, association speed drops as low as 14 FPS for densely populated scenes. This indicates that re-identification CNNs may be impractical for safety critical applications like autonomous driving, especially in urban environments. Simultaneous detection and tracking results suggests an increased tracking robustness. The removal of a complex data association strategy improves robustness with respect to extended modules like re-identification. This indicates that the inherent simplicity in the simultaneous detection and tracking paradigm can provide robust baseline trackers for a variety of applications. We note that further research is required to strengthen this notion.