Multi-ROI Association and Tracking With Belief Functions: Application to Traffic Sign Recognition
—This paper presents an object tracking algorithm using belief functions applied to vision-based traffic sign recognition systems. This algorithm tracks detected sign candidates over time in order to reduce false positives due to data fusion formalization. In the first stage, regions of interest (ROIs) are detected and combined using the transferable belief model semantics. In the second stage, the local pignistic probability algorithm generates the associations maximizing the belief of each pairing between detected ROIs and ROIs tracked by multiple Kalman filters. Finally, the tracks are analyzed to detect false positives. Due to a feedback loop between the multi-ROI tracker and the ROI detector, the solution proposed reduces false positives by up to 45%, whereas computation time remains very low.
This paper promotes a concept that an intelligent system could detect not only the road condition but also the driver's information so that an optimal suggestion (alarm) could be given to the driver or even help to control the car. The car model and the testing is an initial study so it is simple and naturally more information will be followed in the near future.