Sensor Fusion based 3D Target Visual Tracking for Autonomous Vehicles with IMM

This paper proposes an approach for object identification and tracking for autonomous vehicle application. In this scheme, data from the vehicle’s 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 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 model. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.