Vehicle make and model recognition using sparse representation and symmetrical SURFs

This paper proposes a new symmetrical SURF descriptor to detect vehicles on roads and applies the sparse representation for the application of vehicle make-and-model recognition (MMR). To detect vehicles from roads, this paper proposes a symmetry transformation on SURF points to detect all possible matching pairs of symmetrical SURF points. Then, each desired ROI of vehicle can be located very accurately through a projection technique. This scheme provides two advantages; there is no need of background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, i.e., multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To treat the two problems, a dynamic sparse representation scheme is proposed to represent a vehicle model in an over-complete dictionary whose base elements are the training samples themselves. With the dictionary, a novel Hamming distance classification scheme is proposed to classify vehicle makes and models to detailed classes. Because of the sparsity of sparse representation and the nature of Hamming code highly tolerant to noise, different vehicle makes and models can be recognized extreme accurately.