# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### Comparison with Online Learning Algorithms  
#### 3.3.1 CAVIAR Pedestrian Detection  
We used the database CAVIAR [42], which was gathered from surveillance video of a shopping mall in Portugal. In the training stage, 1200 frames used for online learning and 34,705 frames for testing, the same as the other comparison targets. In the testing video, each person in every frame was labeled even if partially occluded. Figure 25 is the definition of the region of interest in this database. We used these settings to perform a comparison with OLAM [24], OC [17], and ORF [19]. OLAM is an online learning system combining bagging and a cascade structure. OC is the online learning algorithm combining Online boosting with Principal components analysis. This method is also a competitive online learning algorithm that is the same as ORF. Compared with the other methods, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Aircrafts** & **Motorcycles** & **Vehicles** & **Faces** & **Average** \\ \hline Constellation model [36] & 9.8\% & 7.5\% & 9.7\% & 3.6\% & 7.7\% \\ \hline HSM and various methods [40] & 6.3\% & 2.7\% & 2.3\% & 9.7\% & 5.3\% \\ \hline Generalized correlogram [41] & 7.5\% & 3.3\% & 4.2\% & 4.8\% & 5.0\% \\ \hline GVC [37] & 2.7\% & 3.2\% & 2.7\% & 2.8\% & 2.9\% \\ \hline Our Average Result & 7.4\% & 2.8\% & 2.3\% & 0.6\% & 3.3\% \\ \hline Our Best Result & 6.2\% & 2.4\% & 1.8\% & 0.6\% & 2.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Enhanced result (error rate) of combination with external aircraft model in Caltech. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Times** & **Aircrafts** & **Motorcycles** & **Vehicles** & **Faces** & **Average** \\ \hline \(1\) & 7.3\% & 2.6\% & 2.8\% & 0.8\% & 3.4\% \\ \hline \(2\) & 8.1\% & 2.5\% & 2.1\% & 0.5\% & 3.3\% \\ \hline \(3\) & 6.2\% & 2.4\% & 1.8\% & 0.6\% & 2.8\% \\ \hline \(4\) & 7.8\% & 2.6\% & 2.1\% & 0.8\% & 3.3\% \\ \hline \(5\) & 6.8\% & 3.2\% & 3.  
many parameters need to be set in ORF. In this experiment, we used five trees, whose largest depths were 20 with 900 random features in each node. Before the split of each node, at least 200 pieces of data need to be considered, and the minimum gain of a split should be less than 0.1. The final result is shown in Table 8.  
OC uses a shape and appearance model to automatically obtain new patterns and update the model, while ORF, OLAM, and our work obtain new patterns by hand labeling. Here, we found that ORF performed less well than our approach, although it also uses hand labeling; the split node in ORF cannot be changed. If it is faced with other important patterns, it cannot adjust the model moderately according to these patterns while updating, thus affecting the performance. In the final results, although we performed less well in terms of precision, we achieved much better recall than the other methods. The proposed method outperformed OLAM, in terms of recall by 6.4% and F-measure by 1.1%. Furthermore, we used fewer frames while learning than the other methods but, in terms of the F-measure, achieved comparable and even better results than the other methods mainly because we adopted dynamic multiple feature allocation to make the feature pool more adaptive to the pattern pool. The results demonstrated the performance of our online learning algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Aircrafts** & **Motorcycles** & **Vehicles** & **Faces** & **Average** \\ \hline GVC [37] & 2.7\% & 3.2\% & 2.7\% & 2.8\% & 2.9\% \\ \hline Our original model & 6.6\% & 2.9\% & 1.6\% & 0.6\% & 2.9\% \\ \hline External model & 5.7\% & 55.5\% & 55.8\% & 47.8\% & 41.2\% \\ \hline Our model with external model (Best result) & 3.6\% & 2.8\% & 1.8\% & 0.6\% & 2.2\% \\ \hline Our model with external model (Average result) & 3.9\% & 2.9\% & 1.8\% & 0.7\% & 2.3\% \\ \hline Our model with external model (Worst result) & 4.1\% & 3.1\% & 1.9\% & 0.8\% & 2.5\% \\ \hline \hline \end{tabular}
\end{table}