Generalizable Online 3D Pedestrian Tracking with Multiple Cameras

3D pedestrian tracking using multiple cameras is still a challenging task with many applications such as surveillance, behavioral analysis, statistical analysis, and more. Many of the existing tracking solutions involve training the algorithms on the target environment, which requires extensive time and effort. We propose an online 3D pedestrian tracking method for multi-camera environments based on a generalizable detection solution that does not require training with data of the target scene. We establish temporal relationships between people detected in different frames by using a combination of graph matching algorithm and Kalman filter. Our proposed method obtained a MOTA and MOTP of 77.1% and 96.4%, respectively on the test split of the public WILDTRACK dataset. Such results correspond to an improvement of approximately 3.4% and 22.2%, respectively, compared to the best existing online technique. Our experiments also demonstrate the advantages of using appearance information to improve the tracking performance.