Tracking and data association
Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Intensity and edge-based symmetry detection with an application to car-following
CVGIP: Image Understanding
Visual perception of obstacles and vehicles for platooning
IEEE Transactions on Intelligent Transportation Systems
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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This paper presents an approach to fuse multiple sensors in an Interacting Multiple Model design. Visual features like shadow and symmetry, treated as independent stand-alone virtual sensors, are employed for detection and tracking of vehicles for driver assistance tasks. Cheap Joint Probabilistic Data Association is utilised to account for the large amount of clutter in the measurements provided by these sensors. Special attention is devoted to the different noise characteristics of the measurements. The individual sensors are considered in a sequential manner, leading to a versatile fusion architecture that allows easy integration of further sensor modules.