A Precise Tracking Algorithm Based on Raw Detector Responses and a Physical Motion Model

We present a method to simultaneously track multiple objects which are subject to physical motion and can be evaluated through raw detector responses in video. Due to their two-staged design, popular tracking-by-detection approaches lack precision in the estimated trajectories due to detector inaccuracies, e.g., lighting, deformation or background clutter. Instead of separating the tasks of detection and tracking, we propose to integrate both in a single probabilistic objective function for determining the object states in a sequence. Both support each other accounting for detection inaccuracies and leading to a robust and precise single target tracker. Based on this, we extend it to multiple targets by solving the problem of determining trajectory limits and sorting out any multiple target ambiguities probabilistically. We apply our method to the task of tracking thrown balls with the goal of accurate trajectory prediction for the purpose of ball catching with a humanoid robot. Our results show improved tracking accuracy with respect to ground truth on average by around 17 %, which is dominated by increased accuracy at the beginning of the trajectory.