# Incremental Canonical Correlation Analysis
## 4 Experiments
### Results
#### 4.2.2 Qualitative Comparison  
In the basketball and football videos, the target is a fast-moving person. For the basketball video, when more than one player grabs the ball, the tracking player is easily affected by the background information. In the football video, in addition to being easily affected by the background information, \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **ICCA** & **IVT** & **LIAPG** & **TLD** & **VTD** & **MIL** & **Frag** \\ \hline Basketball & 31.01 & 22.09 & 2.36 & 19.81 & 13.36 & 30.6 & 2.74 \\ Car4 & 30.8 & 22.1 & 1.6747 & 14.97 & 8.26 & 27.73 & 7.07 \\ Cardark & 33.12 & 32.41 & 3.27 & 25.22 & 18.95 & 27.16 & 6.68 \\ Carscale & 25.01 & 22.23 & 2.59 & 23.79 & 12.96 & 24.66 & 3.47 \\ David3 & 32.71 & 31.65 & 3.01 & 19.65 & 14.10 & 25.45 & 3.19 \\ Deer & 23.7 & 21.9 & 2.1516 & 16.62 & 12.15 & 17.38 & 2.68 \\ Faceocc1 & 28.87 & 27.31 & 4.56 & 17.85 & 13.80 & 27.78 & 4.27 \\ Faceocc2 & 30.95 & 22.49 & 3 & 21.93 & 13.80 & 29.01 & 5.32 \\ Fish & 37.05 & 29.01 & 2.22 & 36.11 & 16.48 & 25.02 & 5.39 \\ Football & 31.53 & 22.06 & 3.44 & 20.99 & 13.67 & 31.56 & 2.87 \\ Crossing & 34.12 & 27.84 & 1.48 & 30.14 & 12.96 & 11.6 & 4.89 \\ Dog1 & 23.03 & 22.86 & 1.66 & 10.03 & 8.44 & 18.67 & 6.71 \\ Jogging-2 & 26.81 & 5.3 & 0.18 & 26.71 & 19.82 & 30.06 & 5.36 \\ Jumping & 29.76 & 8.53 & 1.02 & 10.78 & 14.87 & 29.8 & 7.41 \\ Whyang & 31.34 & 7.31 & 3.17 & 12.09 & 7.76 & 46.3 & 3.15 \\ Mountain Bike & 32.36 & 31.96 & 3.02 & 17.98 & 14.27 & 27.93 & 3.67 \\ Singer1 & 35.79 & 22.49 & 2.29 & 23.19 & 14.49 & 34.56 & 3.44 \\ Walking & 33.08 & 32.22 & 4.34 & 12.45 & 13.52 & 28.34 & 2.19 \\ \hline \end{tabular}
\end{table} 
Table 1: The FPS of the compared trackers on eighteen videos. the helmet information is similar, when the players occlude each other, and the tracked target is easy to lose. Figure 1a,j show good tracking ability of ICCA, which is better than other trackers in the football video. In the three videos of car4, cardark, and carscale, fast-moving cars drive on the streets during the day and night, as well as on the outdoor roads during the day. For the video car4, when the car passes through the overpass or under the shade of trees, its surface light changes greatly. In the video cardark, cars on the street at night are easily blurred by the brightness of other lights or street lights. The car in the video carscale will drive from far to near, and the scale will change when passing through a cluster of branches. Figure 1b-d shows that ICCA tracking results are attractive. In Figure 1e, namely, the video david3, the tracked person walks outdoors. In the process of walking back and forth, it is occluded twice by the trunk. Most tracking algorithms lose the target, while ICCA still works stably. In the deer video sequences, most algorithms fail because the tracked deer is very similar to the surrounding background and moves rapidly; ICCA effectively performs the tracking. The dog in the video Dog1 moves from near to far but slowly, thus most methods can track the target stably. As in Figure 1, the videos faceocc1, faceocc2, and jogging-2 share the common things: the tracked targets are all sometimes occluded, especially the target in jogging-2 has almost complete occlusion, and the color of the occlusion appearance is very similar to the target. ICCA works well and shows its effectiveness. In the video fish, because the camera is shaking slightly, it is more difficult to track the target. ICCA can track the target accurately. In Figure 2a, since the building in the video _crossing_ blocks the sun, the pedestrian tracking is relatively stable in the building shadow. When walking to the shadow and direct light, illumination variations are quite different. In this case, most trackers miss the target, but ICCA can still track the target even when the tracking bounding box size changes a little. In the _singer1_ video, the background clutter affects the effective learning of the subspace. Likewise, ICCA can track the target correctly.