TY - GEN
T1 - An algorithm for real-time object tracking in complex environment
AU - Gao, Dongxu
AU - Cao, Jiangtao
AU - Ju, Zhaojie
PY - 2014/9/4
Y1 - 2014/9/4
N2 - The current sparse representation tracking algorithm is not suitable for the objects that illumination changes, scale changes, the object color is similar with the surrounding region, and occlusion etc, what's more, it is hard to realize real-time tracking for solving an l1 norm related minimization problems. An optimal algorithm is introduced by exploiting an accelerated proximal gradient approach which contains some improvements of particle filter function, sparse representation alterative weights and coefficient. These improvements not only reduce the influences of appearance change but also make the tracker runs in real time. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm has favorably better performance than several state-of-the-art trackers using challenging benchmark image sequences, and significantly reduces the computing cost.
AB - The current sparse representation tracking algorithm is not suitable for the objects that illumination changes, scale changes, the object color is similar with the surrounding region, and occlusion etc, what's more, it is hard to realize real-time tracking for solving an l1 norm related minimization problems. An optimal algorithm is introduced by exploiting an accelerated proximal gradient approach which contains some improvements of particle filter function, sparse representation alterative weights and coefficient. These improvements not only reduce the influences of appearance change but also make the tracker runs in real time. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm has favorably better performance than several state-of-the-art trackers using challenging benchmark image sequences, and significantly reduces the computing cost.
UR - http://www.scopus.com/inward/record.url?scp=84908479880&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889790
DO - 10.1109/IJCNN.2014.6889790
M3 - Conference contribution
AN - SCOPUS:84908479880
T3 - IEEE IJCNN Proceedings Series
SP - 1996
EP - 2002
BT - Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks
Y2 - 6 July 2014 through 11 July 2014
ER -