Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

Abstract

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.