## 4 Experiments
### Taining and Performance Evaluation of the Proposed Model
We understand that they seem to have unique characteristic trajectory patterns for setting, hauling and pulling phases. The SVM-based model gave similar performances to the CNN-based model for the purse seine, longline, and trawl types, but the CNN-based model showed superior performance to the SVM-based model for stow net and trap types. The stow net, longline, and gill net fishing gear types have relatively lower TPI scores for the CNN-based model. They seem to have somewhat similar trajectories in their fishing-time spans. The SVM-based model showed worse performance for traps than for purse seine, stow nets, and longline. Fishing ships of trap type have shorter periods for both fishing gear setting and hauling compared to other fishing gear types, and in addition they slow down for fishing gear setting and then move to other trap sites at a speed of more than 6 knots. Hence, the speed histograms of trap type show tall heights both in a high speed range for fishing gear setting and in a low speed range for fishing gear setting and hauling which are also observed in the histograms for stow net, longline, and drift gill net types. The similarities in the histograms of trap type with those of those types and the absence of trajectory information in the speed histograms seem to make it difficult for the SVM model to tell trap type from those types. The SVM-based model classified correctly 39.9% of traps into traps, but incorrectly 2.1% of traps into purse seine, 15.7% into stow nets, 21.4% into longline, 16.4% into drift gill nets, and 2.1% into trawls. The histogram-based features used for the SVM model seems not to be sufficiently good enough to extract meaningful features for traps. On the contrary, the CNN-based model seems to be better at taking into account the continuous changes in speed and course than the SVM-based model. For the longline type, the SVM-based model showed slightly better performance than the CNN-based model. The CNN-based model seems to make some false classification for trajectory windows of longline type due to the temporarily occurring non-fishing periods and the trajectory patterns partially similar to those of stow net and drift gill net types. As shown in Table 6, however, the performance of CNN-based model for the longline type comes close to that of the SVM-based model when they were evaluated for TPI scores. The performance index DPI identifies the fishing gear type for a given trajectory over a day by taking the major gear type for all trajectory window data of which time span are, even partially, overlapped with the time span of the day. Table 6 shows the DPI scores for the trained model. The total performance of DPI is improved by 0.062 compared to that of TPI. The performance for stow net, longline, and drift gill net gear type has been greatly improved by almost 0.5 or more. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **The Number of Test Data in Days (Percentage)** & **The SVM-Based Model** &
\begin{tabular}{c} **The Proposed** \\ **CNN-Based Model** \\ \end{tabular} \\ \hline Purse Seine & 7111 (21.4\%) & 0.935 & 0.965 \\ Stow Nets & 5226 (15.7\%) & 0.721 & 0.941 \\ Longline & 4195 (12.6\%) & 0.971 & 0.955 \\ Drift Gill Nets & 3077 (9.3\%) & 0.782 & 0.936 \\ Traps & 6060 (18.3\%) & 0.512 & 0.971 \\ Trawls & 7550 (22.7\%) & 0.968 & 0.985 \\ Total & 33,187 & 0.814 & 0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Day-wise performance index (DPI) scores for the SVM-based and the CNN-based model.