This paper presents a new symmetrical SURF descriptor to detect vehicles on roads and then proposes a novel sparsity-based classification scheme to recognize their makes and models. First, for vehicle detection, 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 from the set of symmetrical matching pairs through a projection technique. The advantages of this scheme are no need of background subtraction and its extreme efficiency in real-time detection tasks. After that, two challenges in vehicle make and model recognition (MMR) should be addressed, i.e., the multiplicity and ambiguity problems. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem means vehicles even made from different companies often share 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 the representation and the nature of Hamming code highly tolerant to noise, different vehicle makes and models can be recognized with high accuracy.
- Sparse representation
- Symmetrical SURF
- Vehicle detection
- Vehicle make and model recognition