# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 6 Experiment and Result Analysis
### Qualitative Comparison and Analysis  
In the Ship-1 sequence, the influence of the size change of the target on the identification process is mainly considered. At the 216 th frame, the SVST and MVST gradually separated from the target when the target size just began to increase. At the 365 th frame, the target size has doubled from the initial target size due to the continuous increase invariati the ship size, and the SVST and MVST have lost the target. In the whole process, although L1T, MTT and SVMT kept tracking the target, the effect was not as good as MVMT. Experiments show that MVmt has strong robustness to target size changes.  
In the Ship-2 sequence, the main interference factors are the appearance change and angle change of the target. At the 241 st frame, MVST soon lost its target. At the 389 th and 436 th frames, MTT, SVMT, L1T, and MVMT tracked the target all the way, but SVMT only captured part of the target, which was not as effective as MVMT. The experimental results show that MVMT can deal with the problems of size change, appearance change  
Figure 14: Identification results of 7 algorithms in 6 groups of ship sequence pictures.  
and angle change. L1T and MVST fail to track the whole sequence, which shows that the mechanism of multi-task joint learning makes MTT, SVMT, and MVMT more robust. However, MTT and SVMT are less robust than MVMT because MVMT uses the advantages of complementary features and can detect outliers.  
In the Ship-3 sequence, the main factors are fast motion and blur. At the 821 st frame, the MVST quickly loses the target. At the 931 st frame, after the camera lens moves rapidly, the image becomes blurred. L1T and SVMT only capture some targets, while MVMT keeps locking the targets. Compared with L1T and MVST, MVMT can fully track the target under different changes because of the robustness of additional features. In the whole database-3 sequence, only MVMT can successfully track the target in the whole sequence, while other trackers either completely lose the target or contain most of the background. It is concluded from the experiment that the single-task tracker is easily affected by the appearance change.  
The Ship-4 sequence is mainly tested for occlusion factors. At the 243 rd frame, the target is unobstructed, and all methods lock the target well. However, At the 317 th frame, SVST and MVST have been separated from the target due to partial occlusion of the target. SVMT has included part of the background, and L1T has only captured part of the target. With the occlusion leaving, SVMT, L1T and MTT can track the target at the 532 nd frame, but their performance is not as good as MVMT. This shows that MVMT can better deal with the influence of occlusion factors than other methods.  
In the Ship-5 sequence, the influence of illumination change factor is mainly considered. At the 233 rd frame, MVMT, MTT, and L1T can fully track the sailing ship and only MVST is separated from the target. With the gradual dimming of the light, MVST contains more and more background areas at the 346 th frame. According to the experimental results, MVMT has better robustness than other methods.  
In the Ship-6 sequence, it mainly detects the stability and robustness of target identification on infrared video. At the 222 nd frame, MVST has lost the target. MTT, SVMT, L1T, and MVMT can keep locking the target, but SVMT is easy to contain more background. MVMT can faithfully track the sailing ship, which shows that MVMT is better than other methods and can track infrared video stably.