An Improved l1 Tracker for Transportation Vehicle Tracking
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. l1 tracker 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 the l1-FSAMP algorithm not only improves the running speed, but also reduces the average tracking errors by 83% compared to the l1-OMP algorithm. The results show that the proposed algorithm is suitable for practical real-time tracking of transportation vehicles.
X. Y. Li et al., "An Improved l1 Tracker for Transportation Vehicle Tracking", Applied Mechanics and Materials, Vol. 740, pp. 562-567, 2015