Abstract
Transportation vehicle tracking systems need to equip with a tracking algorithm with not only good tracking accuracy, but also fast computation speed to meet the real time changes of vehicles.l1tracker has good tracking accuracy, but the high computational complexity limits its application in real-time systems. In order to solve this problem, this paper proposed a novel algorithm that utilize compressive sensing to reduce dimensions and improved Sparsity Adaptive Matching Pursuit (SAMP) algorithm to rebuild the coefficients of templates. The experimental results show that thel1-FSAMP algorithm not only improves the running speed, but also reduces the average tracking errors by 83% compared to thel1-OMP algorithm. The results show that the proposed algorithm is suitable for practical real-time tracking of transportation vehicles.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
