@article{(Open Science Index):https://publications.waset.org/pdf/10004496,
	  title     = {A Practical and Efficient Evaluation Function for 3D Model Based Vehicle Matching},
	  author    = {Yuan Zheng},
	  country	= {},
	  institution	= {},
	  abstract     = {3D model-based vehicle matching provides a new way
for vehicle recognition, localization and tracking. Its key is to
construct an evaluation function, also called fitness function, to
measure the degree of vehicle matching. The existing fitness functions
often poorly perform when the clutter and occlusion exist in traffic
scenarios. In this paper, we present a practical and efficient fitness
function. Unlike the existing evaluation functions, the proposed
fitness function is to study the vehicle matching problem from
both local and global perspectives, which exploits the pixel gradient
information as well as the silhouette information. In view of the
discrepancy between 3D vehicle model and real vehicle, a weighting
strategy is introduced to differently treat the fitting of the model’s
wireframes. Additionally, a normalization operation for the model’s
projection is performed to improve the accuracy of the matching.
Experimental results on real traffic videos reveal that the proposed
fitness function is efficient and robust to the cluttered background
and partial occlusion.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {10},
	  number    = {6},
	  year      = {2016},
	  pages     = {973 - 979},
	  ee        = {https://publications.waset.org/pdf/10004496},
	  url   	= {https://publications.waset.org/vol/114},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 114, 2016},
	}