caa a22 4500 605541264 CHVBK 20210128100915.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00371-014-0984-8 doi (NATIONALLICENCE)springer-10.1007/s00371-014-0984-8 Robust tracking via discriminative sparse feature selection [Elektronische Daten] [Jin Zhan, Zhuo Su, Hefeng Wu, Xiaonan Luo] In this paper, we propose a novel generative tracking approach based on discriminative sparse feature selection. The sparse features are the discriminative sparse representation of samples, which are achieved by learning a compact and discriminative dictionary. Besides the target templates, the proposed approach also incorporates the close-background templates to approximate the partial variations. We learn the dictionary and a classifier together, and search the tracking result with the maximum similarity and the minimal reconstruction error criterion using the discrimination of sparse features. In addition, we resample the close-background templates and update the dictionary in an adaptive way during tracking. Experimental results on several challenging video sequences demonstrate that the proposed approach has more favorable performance than the state-of-the-art approaches. Springer-Verlag Berlin Heidelberg, 2014 Object tracking nationallicence Sparse representation nationallicence Template dictionary nationallicence Discriminative sparse feature nationallicence Zhan Jin National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut Su Zhuo National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut Wu Hefeng National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut Luo Xiaonan National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut The Visual Computer Springer Berlin Heidelberg 31/5(2015-05-01), 575-588 0178-2789 31:5<575 2015 31 371 https://doi.org/10.1007/s00371-014-0984-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0984-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhan Jin National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Su Zhuo National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Wu Hefeng National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 700 1- Luo Xiaonan National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science and Technology, Sun Yat-sen University, 510006, Guangzhou, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/5(2015-05-01), 575-588 0178-2789 31:5<575 2015 31 371