This paper presents a robust method for vehicle tracking with a monocular camera. A previously published model-based tracking method uses a particle filter which needs an initial vehicle hypothesis both at system start and in case of a tracking loss. We present a template-based solution using different features to estimate a 3D vehicle pose roughly but fast. Combining model-and template-based object tracking keeps the advantages of each algorithm: Precise estimation of the 3D vehicle pose and velocity combined with a fast (re-) initialization approach. The improved tracking system was evaluated while driving autonomously in urban and unstructured environments. The results show that poorly visible vehicles can be tracked during different weather conditions in real-time.

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Previous publications of our institute describe a robust vehicle tracking system for daylight conditions. This paper presents an improved vehicle tracking system which is able to detect and track the convoy leader also by twilight and night. The primary sensor equipment consists of a daytime camera, a LiDAR and an inertial navigation system. An expansion with a thermal and a lowlight camera was necessary to be robust against any illumination conditions. The system is capable of estimating the relative 3D position and orientation, the velocity and the steering angle of a convoy leader precisely in real-time. This makes it possible to follow the convoy leader's track. Another novelty is coupling a Kalman filter with a particle filter for higher stability and accuracy in vehicle tracking. The tracking system shows excellent functionality while driving more than 50 km fully autonomously in urban-and unstructured environments at night.978-1-4799-8757-3/15/$31.00 ©2015 IEEE

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