# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 5 Results
### Results on Videos
Nevertheless, after few minutes of watching the scene, it managed to learn from frames, which resulted in visual feedback that is different than the system prediction. These samples were used to update the SVM, by adding the upper view samples of the different classes to the existing low altitude angle training samples and refining the hyperplanes defining each class. An example of the learning process can be seen in Figure 12 that shows the classification and confidence level of the VAP model to the several appearances of bicyclists in the scene. It is evident that at the beginning the confidence level is low, but, with time, the system's confidence increases as the reinforcement mechanism learns from past examples. The same frames when running the bottom-up pathway only can be seen in Figure 13. It is clear that the confidence levels for the bicyclists are approximately constant and are lower than those achieved by the VAP model. Furthermore, the classification of the people in the scene gets a lower confidence level, and, in Figure 13d, one person is also misclassified as a plant due to the background of vegetation behind him. Figure 12: Classification results of the VAP model for bicyclists passing through the test scene. Examining the confidence level of the system for (**a**) the first and second bicyclists, (**b**) the third bicyclist, (**c**) the seventh bicyclist, and (**d**) the eight bicyclist shows gradual increase in the performance of the system. The strength of the reinforcement learning mechanism is evident when examining Figure 14. The bicycle rider naturally competes with the person class as it shares many similar features originating from the person riding the bicycle. As a result, the person in the scene is being correctly classified in both cases but yields much higher confidence level with the full VAP model. The ability of the full VAP model to resolve this ambiguity is even more prominent for the bicycle rider, as the full VAP model correctly classifies him, while the non-learning model yields a very low confidence level that results in no recognition decision. Finally, Table 1 quantifies the performance comparison between the VAP model and the FRCNN algorithm. The values in the table are the accumulated mean average precision (mAP) and error rate of the system, after running the VAP model on all video clips in the dataset. These results validate the hypothesis underlying this work and show that including biologically-inspired mechanisms in computer vision systems would yield more robust and better-performing object recognition architectures. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN** & \begin{tabular}{l} **FRCNN** \\ **[mAP]** \\ \end{tabular} & \begin{tabular}{l} **FRCNN** \\ **[Error Rate]** \\ \end{tabular} & \
\begin{tabular}{l} **VAP Model** \\ **[mAP]** \\ \end{tabular} &
\begin{tabular}{l} **VAP Model** \\ **[Error Rate]** \\ \end{tabular} \\ \hline ZF & 64.3\% & 0.32 & 93.0\% & 0.03 \\ VGG1024 & 68.4\% & 0.31 & 95.7\% & 0.03 \\ VGG16 & 82.5\% & 0.17 & 95.9\% & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between the faster RCNN and the VAP model. Figure 13: Classification results of the bottom-up pathway (Faster Region-based Convolutional Neural Network (FRCNN) with ZF model) for bicyclists passing through the test scene. Examining the confidence level of the system for the (**a**) first and second bicyclists, (**b**) the third bicyclist, (**c**) the seventh bicyclist, and (**d**) the eight bicyclist shows constant performance. In addition, one of the people in (d) is misclassified as a plant due to the vegetation behind him.