# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 4 Discussion
### Limitations of Current Methods and Viable Solutions  
In the field of AI, training a model requires an enormous number of annotated datasets, such as ImageNet [74]. Similarly, for the convenience of further research carried out by subsequent researchers, large-scale pig datasets have been sequentially developed and released by preceding researchers [42; 51; 56; 60; 75]. Nevertheless, there currently is no standardized and comprehensive high-quality database available that covers all types of pig postures in a wide range of label formats. Therefore, collaboration between researchers and institutions, as well as open-source data initiatives, are encouraged to construct such a pig posture database for superior consistency and interoperability among different datasets. Another limitation in pig posture datasets is class imbalance that is generally caused by the excess (such as lying posture) or scarcity (such as sitting and mounting posture) of certain classes, which is not under human control, resulting in low detection accuracy of these specific classes [51; 55; 56]. Generating synthetic samples or variations of underrepresented classes, such as flipping, rotating, scaling, or introducing noise, to balance the dataset is one way to approach the problem, but it has a limited effect [56]. From the aspect of models, the transfer learning technique cooperating with ensemble models offers a promising solution to the issue by utilizing pre-trained models or knowledge from related tasks and combining the predictions of multiple models [87]. One of the attempts to tackle the limitation in the pig posture detection domain is to endow specific classes with different weights during training [82], such as weighing minority classes more than other classes. Current detection methods under development primarily rely on a single camera, meaning that only a singular angle of pigs can be observed [42; 56]. Under a single-view system, the phenomenon of adhesion and overlapping among captive pigs in the monitoring image hinders the ability to detect pig postures correctly [57]. Additionally, the differentiation between sternal recumbency and ventral recumbency is still a challenge for \begin{table}
\begin{tabular}{c c} \hline \hline
**Limitation** & **Proposed Solutions** \\ \hline Lack of standardized and comprehensive databases. & Collaboration for database construction and open-source data initiatives \\ \hline Class imbalance. & Employment of data augmentation, transfer learning, and ensemble models \\ \hline Demerit of single-view camera systems. & Adoption of depth cameras or multi-modal models compatible with different views \\ \hline The challenge in multi-pig identification and tracking. & Combination with MOT technique \\ \hline Neglect of temporal information. & Introduction of video-based detection and spatial-temporal model \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The limitations of current pig posture methods and proposed solutions. single top-view 2D visual systems [66]. A viable solution is to substitute depth cameras for 2D cameras or to add 2D cameras aimed in other directions. In actual farm environments, to expand the scope of pig monitoring, multiple cameras with different views have been used. However, existing models specifically designed for single-view posture detection are not compatible with multi-view scenarios due to inadequate learning ability [56]. To solve the dilemma, on the one hand, researchers may design and develop new model architectures by incorporating the multi-modal fusion technique [88] to concurrently process images from different devices. On the other hand, the construction of multi-view pig posture datasets is required for long-term progress. Moreover, identification and tracking of multiple pigs throughout the real-time monitor video are neglected by most of the existing research, and it also has certain challenges due to interactions and occlusions frequently appearing in crowded environments as well as the inherent nature of the similarity in the appearance of pigs.