# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 7 Conclusions  
In this paper, a multi-view multi-task target identification method based on sparse learning is proposed. Extensive experiments were conducted on public datasets and custom video datasets on ship sequence frames. Under different challenging interference factors such as size, illumination, blur, deformation, and occlusion, the experimental results show that the proposed MVMT can better identify the target in both visible and infrared video. Compared with the comparisons method, the performance of the proposed MVMT is the best. In summary, the conclusions of this paper are presented as follows:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequence** & **Ship-1** & **Ship-2** & **Ship-3** & **Ship-4** & **Ship-5** & **Ship-6** \\ \hline STMV & 160.4 & 280.4 & 96.1 & 179.2 & 109.1 & 110.8 \\ \hline MTT & 28.57 & 7.12 & 25.52 & 38.06 & 20.52 & 10.42 \\ \hline MTSV & 30.9 & 20.33 & 32.82 & 24.9 & 21.46 & 32.57 \\ \hline L1T & 28.67 & 6.69 & 21.99 & 50.76 & 12.76 & 11.71 \\ \hline MTMV & **9.21** & **4.56** & **7.00** & **11.04** & **3.01** & **5.99** \\ \hline \hline \end{tabular} The bold indicates that the experimental value of this algorithm is superior to other algorithms.  
\end{table}
Table 1: Average error between results of five comparison methods and real position (pixels).  
Figure 15: 7 Average position error (pixel) of 5 comparison methods.  
1. A target identification method based on machine learning is proposed. The traditional target identification method makes use of less information. In the case of many targets and complex backgrounds, tracking loss and error are common. To break through the limitations of traditional methods, we introduce a machine learning approach and provide a new idea for target identification by using sparse representation, dictionary learning method and particle filter framework;
2. By analyzing the features of targets in the surveillance video, the texture, edge, and geometry invariant features of targets are automatically extracted, and the multi-view feature learning method is introduced to realize the information fusion of multiple features. To obtain better results, a multi-task learning method is proposed based on the multi-view feature for joint learning, which makes full use of the information transfer function between different tasks, makes up for the deficiency of the traditional single learning task, improves the classification and recognition accuracy, and provides a theoretical basis for the accurate and stable identification.  
J.C.: writing--original draft preparation, methodology, and conceptualization, and X.W.: writing--original draft preparation, visualization, validation, and software, and Z.Z.: funding acquisition, supervision and project administration. All authors have read and agreed to the published version of the manuscript.  
This work has no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.