.142. 智能系统学报 第11卷 Mean:65.4463 CR2 Mean:747100 Mean:707238 CR d292313 5i25.2076 Std247166 4an22681 Mean.64.199 CR4 Men:56.3894 St30.6914 CRS Sd27.0634CR Sd318258 6 candidate regions(CR1-CR6) 10 template regions(TI-T10) L1-tracker 0.4 NLSSSC-tracke Original ELSSC-tracke 02 100.040.05 006 0.0 0.11 140.07 320100.i10 00700800的 wse co质iens withN忆SSYC'-rakr Fig.3 Comparisions of sparsity and stability with the original 1,-.NLSSSC-.and our ELSSC-optimization.The sparse coefficients only are accurated to the second decimal place. 3.3 Experimental results for visual target tracking large size of candidates based on the definition of in- We evaluate the investigated algorithms compara-tegral image,the feature extraction of these candidates tively,using the center location errors,the average is so fast,that its cost can be ignored),which makes success rates,and the average frames per second.The it robust for occlusion,scale variation,and blur. results are shown in Figs.4&5 and in Tables 3&4.The NISSST and our original and improved trackers all templates of NISSST,the original ELSST,and the im-work well,when the targets are occluded;our two proved ELSST are shown in Fig.4(g-o).Overall,our trackers work even better. original and improved trackers outperform the other For motion blur,our two trackers work better than state-of-the-art algorithms. IVT and the original l-tracker.Moreover,CovTracking For occlusion,five algorithms,except IVT,func- also reveals its ability to handle blur (e.g.,#4,#9, tion satisfactorily,especially at #206,#366 of the and #38 in Fig.4(d,o).In the former sequence,the Dudek sequence in Fig.4(b)(the head in tracking is animal runs and jumps fast (motion blur)with a lot of covered by the hand and glasses),#143,#265,#496 water splashing occlusion),while in the latter,the of the Faceocc2 sequence in Fig.4(c)(the head in man ropes skipping and the camera cannot take the tracking is covered by the book),#85,#108,#433 of clear face of the man.IVT and I,-tracker fail both from the Girl sequence in Fig.4(e)(the head in tracking #4 in Fig.4(d),and never recover after that.Our o- turns right,turns back,and blocks someone else),riginal and improved ELSS lost the target in #31 and and #56,#104,#301 of the Face sequence in Fig.4 #41,then recovered in #33 and #44 Fig.4(d)).In (i)(the head in tracking is also covered by the #12 to #21 and #44 to #71,the improved ELSST works book).After the target recovers from occlusion,these better than original ELSST,CovTracking,I-tracker, five trackers can seek it quickly.IVT works poorly,e- and NLSSST. ven loses the target in #10 of the Girl sequence Fig.5 For rotation and scale variation,our trackers also (e)),because the number of positive and negative perform robustly (Figs.4(a,c,e,g,j)and 5(a,c,e, samples is limited (considering the learning g,j).When the surfer falls forward and backward,the efficiency),and the incremental updating of the girl turns left and right,moves towards and away from classifier in IVT is less effective.CovTracking has a the camera,the man turns left and right,the car turnsＦｉｇ． ３ Ｃｏｍｐａｒｉｓｉｏｎｓ ｏｆ ｓｐａｒｓｉｔｙ ａｎｄ ｓｔａｂｉｌｉｔｙ ｗｉｔｈ ｔｈｅ ｏｒｉｇｉｎａｌ ｌ １ ⁃， ＮＬＳＳＳＣ⁃， ａｎｄ ｏｕｒ ＥＬＳＳＣ⁃ｏｐｔｉｍｉｚａｔｉｏｎ． Ｔｈｅ ｓｐａｒｓｅ ｃｏｅｆｆｉｃｉｅｎｔｓ ｏｎｌｙ ａｒｅ ａｃｃｕｒａｔｅｄ ｔｏ ｔｈｅ ｓｅｃｏｎｄ ｄｅｃｉｍａｌ ｐｌａｃｅ． ３．３ Ｅｘｐｅｒｉｍｅｎｔａｌ ｒｅｓｕｌｔｓ ｆｏｒ ｖｉｓｕａｌ ｔａｒｇｅｔ ｔｒａｃｋｉｎｇ Ｗｅ ｅｖａｌｕａｔｅ ｔｈｅ ｉｎｖｅｓｔｉｇａｔｅｄ ａｌｇｏｒｉｔｈｍｓ ｃｏｍｐａｒａ⁃ ｔｉｖｅｌｙ， ｕｓｉｎｇ ｔｈｅ ｃｅｎｔｅｒ ｌｏｃａｔｉｏｎ ｅｒｒｏｒｓ， ｔｈｅ ａｖｅｒａｇｅ ｓｕｃｃｅｓｓ ｒａｔｅｓ， ａｎｄ ｔｈｅ ａｖｅｒａｇｅ ｆｒａｍｅｓ ｐｅｒ ｓｅｃｏｎｄ． Ｔｈｅ ｒｅｓｕｌｔｓ ａｒｅ ｓｈｏｗｎ ｉｎ Ｆｉｇｓ． ４＆５ ａｎｄ ｉｎ Ｔａｂｌｅｓ ３＆４． Ｔｈｅ ｔｅｍｐｌａｔｅｓ ｏｆ ＮＬＳＳＳＴ， ｔｈｅ ｏｒｉｇｉｎａｌ ＥＬＳＳＴ， ａｎｄ ｔｈｅ ｉｍ⁃ ｐｒｏｖｅｄ ＥＬＳＳＴ ａｒｅ ｓｈｏｗｎ ｉｎ Ｆｉｇ． ４（ｇ⁃ｏ）． Ｏｖｅｒａｌｌ， ｏｕｒ ｏｒｉｇｉｎａｌ ａｎｄ ｉｍｐｒｏｖｅｄ ｔｒａｃｋｅｒｓ ｏｕｔｐｅｒｆｏｒｍ ｔｈｅ ｏｔｈｅｒ ｓｔａｔｅ⁃ｏｆ⁃ｔｈｅ⁃ａｒｔ ａｌｇｏｒｉｔｈｍｓ． Ｆｏｒ ｏｃｃｌｕｓｉｏｎ， fiｖｅ ａｌｇｏｒｉｔｈｍｓ， ｅｘｃｅｐｔ ＩＶＴ， ｆｕｎｃ⁃ ｔｉｏｎ ｓａｔｉｓｆａｃｔｏｒｉｌｙ， ｅｓｐｅｃｉａｌｌｙ ａｔ ＃ ２０６， ＃ ３６６ ｏｆ ｔｈｅ Ｄｕｄｅｋ ｓｅｑｕｅｎｃｅ ｉｎ Ｆｉｇ． ４ （ｂ） （ｔｈｅ ｈｅａｄ ｉｎ ｔｒａｃｋｉｎｇ ｉｓ ｃｏｖｅｒｅｄ ｂｙ ｔｈｅ ｈａｎｄ ａｎｄ ｇｌａｓｓｅｓ）， ＃１４３， ＃２６５， ＃４９６ ｏｆ ｔｈｅ Ｆａｃｅｏｃｃ２ ｓｅｑｕｅｎｃｅ ｉｎ Ｆｉｇ． ４ （ ｃ） （ ｔｈｅ ｈｅａｄ ｉｎ ｔｒａｃｋｉｎｇ ｉｓ ｃｏｖｅｒｅｄ ｂｙ ｔｈｅ ｂｏｏｋ）， ＃８５， ＃１０８， ＃４３３ ｏｆ ｔｈｅ Ｇｉｒｌ ｓｅｑｕｅｎｃｅ ｉｎ Ｆｉｇ ．４ （ｅ） （ｔｈｅ ｈｅａｄ ｉｎ ｔｒａｃｋｉｎｇ ｔｕｒｎｓ ｒｉｇｈｔ， ｔｕｒｎｓ ｂａｃｋ， ａｎｄ ｂｌｏｃｋｓ ｓｏｍｅｏｎｅ ｅｌｓｅ）， ａｎｄ ＃５６， ＃１０４， ＃３０１ ｏｆ ｔｈｅ Ｆａｃｅ ｓｅｑｕｅｎｃｅ ｉｎ Ｆｉｇ． ４ （ ｉ ） （ ｔｈｅ ｈｅａｄ ｉｎ ｔｒａｃｋｉｎｇ ｉｓ ａｌｓｏ ｃｏｖｅｒｅｄ ｂｙ ｔｈｅ ｂｏｏｋ）． Ａｆｔｅｒ ｔｈｅ ｔａｒｇｅｔ ｒｅｃｏｖｅｒｓ ｆｒｏｍ ｏｃｃｌｕｓｉｏｎ， ｔｈｅｓｅ fiｖｅ ｔｒａｃｋｅｒｓ ｃａｎ ｓｅｅｋ ｉｔ ｑｕｉｃｋｌｙ． ＩＶＴ ｗｏｒｋｓ ｐｏｏｒｌｙ， ｅ⁃ ｖｅｎ ｌｏｓｅｓ ｔｈｅ ｔａｒｇｅｔ ｉｎ ＃１０ ｏｆ ｔｈｅ Ｇｉｒｌ ｓｅｑｕｅｎｃｅ （Ｆｉｇ． ５ （ｅ））， ｂｅｃａｕｓｅ ｔｈｅ ｎｕｍｂｅｒ ｏｆ ｐｏｓｉｔｉｖｅ ａｎｄ ｎｅｇａｔｉｖｅ ｓａｍｐｌｅｓ ｉｓ ｌｉｍｉｔｅｄ （ ｃｏｎｓｉｄｅｒｉｎｇ ｔｈｅ ｌｅａｒｎｉｎｇ ｅffiｃｉｅｎｃｙ）， ａｎｄ ｔｈｅ ｉｎｃｒｅｍｅｎｔａｌ ｕｐｄａｔｉｎｇ ｏｆ ｔｈｅ ｃｌａｓｓｉfiｅｒ ｉｎ ＩＶＴ ｉｓ ｌｅｓｓ ｅffｅｃｔｉｖｅ． ＣｏｖＴｒａｃｋｉｎｇ ｈａｓ ａ ｌａｒｇｅ ｓｉｚｅ ｏｆ ｃａｎｄｉｄａｔｅｓ （ｂａｓｅｄ ｏｎ ｔｈｅ ｄｅfiｎｉｔｉｏｎ ｏｆ ｉｎ⁃ ｔｅｇｒａｌ ｉｍａｇｅ， ｔｈｅ ｆｅａｔｕｒｅ ｅｘｔｒａｃｔｉｏｎ ｏｆ ｔｈｅｓｅ ｃａｎｄｉｄａｔｅｓ ｉｓ ｓｏ ｆａｓｔ， ｔｈａｔ ｉｔｓ ｃｏｓｔ ｃａｎ ｂｅ ｉｇｎｏｒｅｄ）， ｗｈｉｃｈ ｍａｋｅｓ ｉｔ ｒｏｂｕｓｔ ｆｏｒ ｏｃｃｌｕｓｉｏｎ， ｓｃａｌｅ ｖａｒｉａｔｉｏｎ， ａｎｄ ｂｌｕｒ． ＮＬＳＳＳＴ ａｎｄ ｏｕｒ ｏｒｉｇｉｎａｌ ａｎｄ ｉｍｐｒｏｖｅｄ ｔｒａｃｋｅｒｓ ａｌｌ ｗｏｒｋ ｗｅｌｌ， ｗｈｅｎ ｔｈｅ ｔａｒｇｅｔｓ ａｒｅ ｏｃｃｌｕｄｅｄ； ｏｕｒ ｔｗｏ ｔｒａｃｋｅｒｓ ｗｏｒｋ ｅｖｅｎ ｂｅｔｔｅｒ． Ｆｏｒ ｍｏｔｉｏｎ ｂｌｕｒ， ｏｕｒ ｔｗｏ ｔｒａｃｋｅｒｓ ｗｏｒｋ ｂｅｔｔｅｒ ｔｈａｎ ＩＶＴ ａｎｄ ｔｈｅ ｏｒｉｇｉｎａｌ ｌ １ ⁃ｔｒａｃｋｅｒ． Ｍｏｒｅｏｖｅｒ， ＣｏｖＴｒａｃｋｉｎｇ ａｌｓｏ ｒｅｖｅａｌｓ ｉｔｓ ａｂｉｌｉｔｙ ｔｏ ｈａｎｄｌｅ ｂｌｕｒ （ ｅ． ｇ．， ＃４， ＃９， ａｎｄ ＃３８ ｉｎ Ｆｉｇ． ４（ｄ，ｏ）． Ｉｎ ｔｈｅ ｆｏｒｍｅｒ ｓｅｑｕｅｎｃｅ， ｔｈｅ ａｎｉｍａｌ ｒｕｎｓ ａｎｄ ｊｕｍｐｓ ｆａｓｔ （ｍｏｔｉｏｎ ｂｌｕｒ） ｗｉｔｈ ａ ｌｏｔ ｏｆ ｗａｔｅｒ ｓｐｌａｓｈｉｎｇ （ ｏｃｃｌｕｓｉｏｎ）， ｗｈｉｌｅ ｉｎ ｔｈｅ ｌａｔｔｅｒ， ｔｈｅ ｍａｎ ｒｏｐｅｓ ｓｋｉｐｐｉｎｇ ａｎｄ ｔｈｅ ｃａｍｅｒａ ｃａｎｎｏｔ ｔａｋｅ ｔｈｅ ｃｌｅａｒ ｆａｃｅ ｏｆ ｔｈｅ ｍａｎ． ＩＶＴ ａｎｄ ｌ １ ⁃ｔｒａｃｋｅｒ ｆａｉｌ ｂｏｔｈ ｆｒｏｍ ＃４ ｉｎ Ｆｉｇ． ４（ｄ）， ａｎｄ ｎｅｖｅｒ ｒｅｃｏｖｅｒ ａｆｔｅｒ ｔｈａｔ． Ｏｕｒ ｏ⁃ ｒｉｇｉｎａｌ ａｎｄ ｉｍｐｒｏｖｅｄ ＥＬＳＳ ｌｏｓｔ ｔｈｅ ｔａｒｇｅｔ ｉｎ ＃３１ ａｎｄ ＃４１， ｔｈｅｎ ｒｅｃｏｖｅｒｅｄ ｉｎ ＃３３ ａｎｄ ＃４４ （Ｆｉｇ． ４（ｄ））． Ｉｎ ＃１２ ｔｏ ＃２１ ａｎｄ ＃４４ ｔｏ ＃７１， ｔｈｅ ｉｍｐｒｏｖｅｄ ＥＬＳＳＴ ｗｏｒｋｓ ｂｅｔｔｅｒ ｔｈａｎ ｏｒｉｇｉｎａｌ ＥＬＳＳＴ， ＣｏｖＴｒａｃｋｉｎｇ， ｌ １ ⁃ｔｒａｃｋｅｒ， ａｎｄ ＮＬＳＳＳＴ． Ｆｏｒ ｒｏｔａｔｉｏｎ ａｎｄ ｓｃａｌｅ ｖａｒｉａｔｉｏｎ， ｏｕｒ ｔｒａｃｋｅｒｓ ａｌｓｏ ｐｅｒｆｏｒｍ ｒｏｂｕｓｔｌｙ （Ｆｉｇｓ． ４（ａ，ｃ，ｅ，ｇ，ｊ） ａｎｄ ５（ ａ，ｃ，ｅ， ｇ，ｊ）． Ｗｈｅｎ ｔｈｅ ｓｕｒｆｅｒ ｆａｌｌｓ ｆｏｒｗａｒｄ ａｎｄ ｂａｃｋｗａｒｄ， ｔｈｅ ｇｉｒｌ ｔｕｒｎｓ ｌｅｆｔ ａｎｄ ｒｉｇｈｔ， ｍｏｖｅｓ ｔｏｗａｒｄｓ ａｎｄ ａｗａｙ ｆｒｏｍ ｔｈｅ ｃａｍｅｒａ， ｔｈｅ ｍａｎ ｔｕｒｎｓ ｌｅｆｔ ａｎｄ ｒｉｇｈｔ， ｔｈｅ ｃａｒ ｔｕｒｎｓ ·１４２· 智 能 系 统 学 报 第 １１ 卷