1WANG Hongyuan,et al:Efficient tracker based on sparse coding with Euclidean local structure-based constraint .143. over,and the juice bottle becomes bigger and smaller to evaluate the success rate.In general,from the above in Surfer,Girl,Faceocc2,Car,and Juice sequence, analysis,we find that our original and improved respectively,five trackers except IVT perform well,es- ELSSC-trackers perform almost the same,and the for- pecially the NLSSS-tracker and our two ELSSC-trackers. mer is slightly better,especially in the Dudek, In a complex background and with high illumina- Faceoce2,Surfer,Stone,CarDark,and Jumping se- tion variance Fig.4(f)),there are many similar quences (Fig.5(a,b,c,f,n,o).However,we also stones to track.The I,-tracker and our two trackers find from Table 4,which summarizes the average work better than other three trackers.Cov-tracker fails, frames per second,that the improved ELSST works because it extracts edge information of targets as one much faster than the original ELSST and almost all the dimension of features,and in this sequences,edge of other trackers;IVT is faster than the improved ELSST targets are ambiguous and hard to be distinct.Similar when dealing with Surfer and Dudek sequences,but its results are obtained from Fig.4(h,1,m). success rate is much worse than that of the improved Table 3 summarizes the average success rates. ELSST.It is sensitive under the phenomena of occlu- Given the tracking results R and the ground-truth Rc, sion,rotation,and target motion blur.The original I,- we use the detection criterion in the PASCAL VOC tracker performs well in most frames,but it is also challenget],i.e., time-consuming and fails to track sometimes;Cov- area(Rr∩Rc) Tracking is suitable for occlusion and rotation,but fails score area(Rr URc) when facing a complex background. #219 14 出 (a)Sequence surfer (375 frames) (b)Sequence dudek (1145 frames) (c)Sequence faceoce2 (819 frames) *2 (d)Sequence animal (71 frames) (e)Sequence girl(500 frames) (f)Sequence S tone (593 frames) 106 175 231 (g)Sequence car(374 frames) (h)Sequence cup(303 frames (i)Sequence face (415 frames) 5【年 (j)Sequence juice (404 frames) (k)Sequence singer(351 frames) (1)Sequence sunshade (172 frames)ｏｖｅｒ， ａｎｄ ｔｈｅ ｊｕｉｃｅ ｂｏｔｔｌｅ ｂｅｃｏｍｅｓ ｂｉｇｇｅｒ ａｎｄ ｓｍａｌｌｅｒ ｉｎ Ｓｕｒｆｅｒ， Ｇｉｒｌ， Ｆａｃｅｏｃｃ２， Ｃａｒ， ａｎｄ Ｊｕｉｃｅ ｓｅｑｕｅｎｃｅ， ｒｅｓｐｅｃｔｉｖｅｌｙ， fiｖｅ ｔｒａｃｋｅｒｓ ｅｘｃｅｐｔ ＩＶＴ ｐｅｒｆｏｒｍ ｗｅｌｌ， ｅｓ⁃ ｐｅｃｉａｌｌｙ ｔｈｅ ＮＬＳＳＳ⁃ｔｒａｃｋｅｒ ａｎｄ ｏｕｒ ｔｗｏ ＥＬＳＳＣ⁃ｔｒａｃｋｅｒｓ． Ｉｎ ａ ｃｏｍｐｌｅｘ ｂａｃｋｇｒｏｕｎｄ ａｎｄ ｗｉｔｈ ｈｉｇｈ ｉｌｌｕｍｉｎａ⁃ ｔｉｏｎ ｖａｒｉａｎｃｅ （ Ｆｉｇ． ４ （ ｆ））， ｔｈｅｒｅ ａｒｅ ｍａｎｙ ｓｉｍｉｌａｒ ｓｔｏｎｅｓ ｔｏ ｔｒａｃｋ． Ｔｈｅ ｌ １ ⁃ｔｒａｃｋｅｒ ａｎｄ ｏｕｒ ｔｗｏ ｔｒａｃｋｅｒｓ ｗｏｒｋ ｂｅｔｔｅｒ ｔｈａｎ ｏｔｈｅｒ ｔｈｒｅｅ ｔｒａｃｋｅｒｓ． Ｃｏｖ⁃ｔｒａｃｋｅｒ ｆａｉｌｓ， ｂｅｃａｕｓｅ ｉｔ ｅｘｔｒａｃｔｓ ｅｄｇｅ ｉｎｆｏｒｍａｔｉｏｎ ｏｆ ｔａｒｇｅｔｓ ａｓ ｏｎｅ ｄｉｍｅｎｓｉｏｎ ｏｆ ｆｅａｔｕｒｅｓ， ａｎｄ ｉｎ ｔｈｉｓ ｓｅｑｕｅｎｃｅｓ， ｅｄｇｅ ｏｆ ｔａｒｇｅｔｓ ａｒｅ ａｍｂｉｇｕｏｕｓ ａｎｄ ｈａｒｄ ｔｏ ｂｅ ｄｉｓｔｉｎｃｔ． Ｓｉｍｉｌａｒ ｒｅｓｕｌｔｓ ａｒｅ ｏｂｔａｉｎｅｄ ｆｒｏｍ Ｆｉｇ． ４（ｈ，ｌ，ｍ）． Ｔａｂｌｅ ３ ｓｕｍｍａｒｉｚｅｓ ｔｈｅ ａｖｅｒａｇｅ ｓｕｃｃｅｓｓ ｒａｔｅｓ． Ｇｉｖｅｎ ｔｈｅ ｔｒａｃｋｉｎｇ ｒｅｓｕｌｔｓ ＲＴ ａｎｄ ｔｈｅ ｇｒｏｕｎｄ⁃ｔｒｕｔｈ ＲＧ ， ｗｅ ｕｓｅ ｔｈｅ ｄｅｔｅｃｔｉｏｎ ｃｒｉｔｅｒｉｏｎ ｉｎ ｔｈｅ ＰＡＳＣＡＬ ＶＯＣ ｃｈａｌｌｅｎｇｅ ［１６］ ，ｉ．ｅ．， ｓｃｏｒｅ ＝ ａｒｅａ（ＲＴ ∩ ＲＧ ） ａｒｅａ（ＲＴ ∪ ＲＧ ） ｔｏ ｅｖａｌｕａｔｅ ｔｈｅ ｓｕｃｃｅｓｓ ｒａｔｅ． Ｉｎ ｇｅｎｅｒａｌ， ｆｒｏｍ ｔｈｅ ａｂｏｖｅ ａｎａｌｙｓｉｓ， ｗｅ fiｎｄ ｔｈａｔ ｏｕｒ ｏｒｉｇｉｎａｌ ａｎｄ ｉｍｐｒｏｖｅｄ ＥＬＳＳＣ⁃ｔｒａｃｋｅｒｓ ｐｅｒｆｏｒｍ ａｌｍｏｓｔ ｔｈｅ ｓａｍｅ， ａｎｄ ｔｈｅ ｆｏｒ⁃ ｍｅｒ ｉｓ ｓｌｉｇｈｔｌｙ ｂｅｔｔｅｒ， ｅｓｐｅｃｉａｌｌｙ ｉｎ ｔｈｅ Ｄｕｄｅｋ， Ｆａｃｅｏｃｃ２， Ｓｕｒｆｅｒ， Ｓｔｏｎｅ， ＣａｒＤａｒｋ， ａｎｄ Ｊｕｍｐｉｎｇ ｓｅ⁃ ｑｕｅｎｃｅｓ （Ｆｉｇ． ５ （ ａ， ｂ， ｃ， ｆ， ｎ， ｏ）． Ｈｏｗｅｖｅｒ， ｗｅ ａｌｓｏ fiｎｄ ｆｒｏｍ Ｔａｂｌｅ ４， ｗｈｉｃｈ ｓｕｍｍａｒｉｚｅｓ ｔｈｅ ａｖｅｒａｇｅ ｆｒａｍｅｓ ｐｅｒ ｓｅｃｏｎｄ， ｔｈａｔ ｔｈｅ ｉｍｐｒｏｖｅｄ ＥＬＳＳＴ ｗｏｒｋｓ ｍｕｃｈ ｆａｓｔｅｒ ｔｈａｎ ｔｈｅ ｏｒｉｇｉｎａｌ ＥＬＳＳＴ ａｎｄ ａｌｍｏｓｔ ａｌｌ ｔｈｅ ｏｔｈｅｒ ｔｒａｃｋｅｒｓ； ＩＶＴ ｉｓ ｆａｓｔｅｒ ｔｈａｎ ｔｈｅ ｉｍｐｒｏｖｅｄ ＥＬＳＳＴ ｗｈｅｎ ｄｅａｌｉｎｇ ｗｉｔｈ Ｓｕｒｆｅｒ ａｎｄ Ｄｕｄｅｋ ｓｅｑｕｅｎｃｅｓ， ｂｕｔ ｉｔｓ ｓｕｃｃｅｓｓ ｒａｔｅ ｉｓ ｍｕｃｈ ｗｏｒｓｅ ｔｈａｎ ｔｈａｔ ｏｆ ｔｈｅ ｉｍｐｒｏｖｅｄ ＥＬＳＳＴ． Ｉｔ ｉｓ ｓｅｎｓｉｔｉｖｅ ｕｎｄｅｒ ｔｈｅ ｐｈｅｎｏｍｅｎａ ｏｆ ｏｃｃｌｕ⁃ ｓｉｏｎ， ｒｏｔａｔｉｏｎ， ａｎｄ ｔａｒｇｅｔ ｍｏｔｉｏｎ ｂｌｕｒ． Ｔｈｅ ｏｒｉｇｉｎａｌ ｌ １ ⁃ ｔｒａｃｋｅｒ ｐｅｒｆｏｒｍｓ ｗｅｌｌ ｉｎ ｍｏｓｔ ｆｒａｍｅｓ， ｂｕｔ ｉｔ ｉｓ ａｌｓｏ ｔｉｍｅ⁃ｃｏｎｓｕｍｉｎｇ ａｎｄ ｆａｉｌｓ ｔｏ ｔｒａｃｋ ｓｏｍｅｔｉｍｅｓ； Ｃｏｖ⁃ Ｔｒａｃｋｉｎｇ ｉｓ ｓｕｉｔａｂｌｅ ｆｏｒ ｏｃｃｌｕｓｉｏｎ ａｎｄ ｒｏｔａｔｉｏｎ， ｂｕｔ ｆａｉｌｓ ｗｈｅｎ ｆａｃｉｎｇ ａ ｃｏｍｐｌｅｘ ｂａｃｋｇｒｏｕｎｄ． 第 １ 期 ＷＡＮＧ Ｈｏｎｇｙｕａｎ， ｅｔ ａｌ： Ｅｆｆｉｃｉｅｎｔ ｔｒａｃｋｅｒ ｂａｓｅｄ ｏｎ ｓｐａｒｓｅ ｃｏｄｉｎｇ ｗｉｔｈ Ｅｕｃｌｉｄｅａｎ ｌｏｃａｌ ｓｔｒｕｃｔｕｒｅ⁃ｂａｓｅｄ ｃｏｎｓｔｒａｉｎｔ ·１４３·