Smart road vehicle sensing system based on monocular vision

In this paper, a smart monocular vision based system to sense vehicles with a camera mounted inside a moving car is developed. The “smartness” is that the sensing ability of our system can be self improved when used. This system maintains an online learning ability which consists of two main stages: an initialization stage by applying an offline trained classifier and a retraining stage with queried and labeled new samples. The unlabeled examples are queried base on “most uncertainty” criterion, and an automatic labeling mechanism is used to assign a class label to some of the queried examples. Finally, the newly labeled training examples are then used to retrain the classifier and improve its performance continuously. Experiments show that the developed system maintains smart learning ability and performs well on real road situation.