3D object tracking in driving environment: A short review and a benchmark dataset
Research in autonomous driving has matured significantly with major automotive companies making important efforts to have their autonomous or highly automated cars available in the near future. As driverless cars move from laboratories to complex real world environments, the perception capabilities of these systems to acquire, model and interpret the 3D spatial information must become more robust. Object tracking is one of the challenging problems of autonomous driving in 3D dynamic environments. Although different approaches are proposed for object tracking with demonstrated success in driving environments, it is very difficult to evaluate and compare them because they are defined with various constraints and boundary conditions. The appearance modeling for object tracking in the driving environments, using a multimodal perception system of autonomous cars and advanced driver assistance systems (ADASs) and the evaluation of such object trackers are the research focus of this paper. A benchmark dataset, called 3D Object Tracking in Driving Environment (3D OTD) is also proposed to facilitate the evaluation of object appearance modeling in object tracking methods.
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)