Vision based autonomous vehicles target visual tracking with multiple dynamics models

An approach is proposed for vision based object identification and tracking for autonomous vehicle applications. In this scheme, data from the vehicles onboard vision and motion sensors are fused to identify the target 3D dynamic features in the world coordinate. Here several simple and basic linear dynamic models are combined to make the approximation of the target's unpredicted or complex motion properties. With these basic linear dynamic models a detailed description of the 3D vision based target tracking system with the interacting multiple models (IMM) for extended Kalman filtering is presented. The target's final state estimates are obtained as a weighted combination of the outputs from each different models. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.