# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 6 Experiment and Result Analysis
### Experimental Results and Analysis of Public Datasets  
To evaluate the effectiveness of the proposed MVMT method, three complementary features are adopted. Four widely used sequence diagrams (car4, faceocc2, david_interior, sylv) are selected. The experimental results of the common dataset are shown in Figure 12.  
In the car4 sequence, the main influencing factors are illumination change and size change. The MVMT and the fast-moving vehicle are tracked from beginning to end, while the SVST and MVST are affected by illumination and size change of the vehicle. They contain more and more background areas, and the target is gradually lost. The target has been completely lost in 194 frames. The SVMT has good performance on the influence of illumination, but at about the 310 th frame the vehicle passes the bridge and suddenly loses the target. According to the experimental results, MVMT ratio can better deal with the factors of light change and target size change.  
In the faceocc2 sequence, the task is to identify the swing of [PERSON]'s head in the room. The main factors of this sequence are swing and occlusion. MVMT and MVST can identify the target successfully in the whole sequence, but MVMT can only identify a small part of the target after the target is occluded many times. In addition, after the head target swings, the appearance and angle change. SVST and SVMT algorithms lose some information of the target. Then after being occluded many times, SVST completely loses the target. The experimental results show that MVMT can better deal with the occlusion, appearance change and angle change in face tasks.  
In the David_indoor sequence, the influence of different illumination is mainly studied. The experimental results show that in the case of dark light, that is, in the early stage of target identification, SVST has been separated from the target because the texture features or edge features are not obvious. With the significant enhancement of illumination, the  
Figure 12: The identification results of the proposed MVMT and SVST, MVST, SVMT algorithms.  
SVMT and MVST algorithms also slowly lose their target. In the whole target identification process, only MVMT algorithm has captured the target. The experimental results show that MVMT can better deal with the problems caused by light changes.  
In the owl sequence, the task is to identify the owl doll. The owl sequence is relatively more fixed, and fuzzy scenes and fast motion are considered. At the early stage of the sequence frame the shape of the target changes and receives different illumination. The SVST and SVMT algorithms lose the target. With the rapid movement, the target becomes fuzzy. The MVST algorithm gradually loses the target information and cannot capture the target. In the whole owl sequence, only MVMT algorithm has captured the target. The experimental results show that MVMT can better deal with the problem of fuzzy targets.  
Figure 13 shows the experimental error comparison curve. It can be seen that the identification methods of SVST and MVST are very poor, and the SVST method is the worst. The reason is that a single task lacks associated information with other tasks and is artificially divided. At the same time, the identification performance of both SVMT method and MVMT method is generally better, because the multi-task learning method cannot only capture the common features among multiple tasks but also eliminate outliers, which makes the performance of identification better. Among them, the MVMT method has the best identification performance, because the multi-view and multi-task simultaneously contain a variety of feature vectors in different state spaces, provide multiple views for the same target object, and learn together, which has very good stability and strong robustness.