Multiple people tracking using contextual trajectory forecasting
Pranav Mantini, Shishir K. Shah · 2016
People tracking is the ability to identify the position of a specified person in the camera view with the progression of time. Trajectory forecasting is the task of predicting the likely path that a person might take to reach a destination. Contextual trajectory forecasting (CTF) leverages the 3D geometric information and static objects in the environment along with observed behavioral norms for human path prediction. In this paper, we enhance CTF to also account for dynamic objects in the environment (like other humans) for prediction. The proposed tracking algorithm makes use of traditional HSV histogram appearance features for detection and combines it with the enhanced CTF for tracking. A maximum likelihood minimum mean square error data association filter is used to probabilistically associate the appearance detections and the CTF predictions for tracking. Two real world scenarios with 49 ID's were used to evaluate the proposed algorithm. The result show a significant improvement over a baseline tracking algorithm (HSV histogram) and a state-of-the-art online multi person tracking algorithm.