# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 5 Results
### Results on Videos  
The evaluation of the VAP model capability to improve with time was done on a set of 39 short video clips, filmed during a period of few weeks from a fixed camera located on the third floor. The position was chosen in order to generate a view angle that is different then the common view angle in the training set that was used to pre-train the FRCNN model. As the view angle in the training set is usually in the vicinity of a zero angle (camera in the same height of the objects), placing the camera on the third floor made the objects look different (top view vs. front view). Hence, we can observe and analyze the learning process of the VAP model, which needs to start with somewhat low recognition scores and gradually improve with time, based on its reinforcement learning mechanism. The video clips were taken over a period of few weeks in order to capture the scene in different lighting, weather conditions and object arrangements. Each video clip was 45-150 s long, which resulted in a 30-min-long test set with 45,613 instances of 181 moving objects, mainly belonging to the categories: Car, Person, Bicycle, and Cat. The overall performance evaluation was done three stages, which we named: \"Bottom-up VGG1024\", \"Contextual pathways included\", and \"Full VAP model\", where the \"Bottom-up VGG1024\" represent the results with the FRCNN model only, the \"Contextual pathways included\" represents the VAP model that integrates the top-down contextual priming with the bottom-up FRCNN process, and the \"Full VAP model\" represents the addition of the Object-file mechanism and the reinforcement-learning scheme. For further research, the video clips are available at: VAP's video dataset. For the evaluation of the classifier's performance, the error rate was calculated by dividing the number of instances with false classification by the total number of instances. In Figure 10, we can see that the proposed model outperformed the buttom-up model performance. This figure shows the cumulative error rate, where the error rate is measured as the sum of false classified instances divided by the total number of instances. It is clear that the VAP model is learning from past examples, improves its accuracy, and outperforms the bottom-up FRCNN classifier (with VGG1024 model). Furthermore, even without applying the reinforcement learning, the model uses the contextual information it learned from the SUN2012 dataset and uses this prior information to perform better than the bottom-up process alone. Figure 9: Overall comparison between using the complete VAP model and using only the bottom-up pathway (VGG16). Precision, recall, and F1-score are shown. These results emphasize the importance of the Object-file module for video sources as a tool to retain perceptual continuity, as well as to enable the reinforcement learning. Comparing the results between Figure 10, with the shallower VGG1024, and Figure 11, with the deeper VGG16, suggests that the superior performance of the proposed model is especially prominent for shallow CNNs with reduced performance. When using shallow CNNs, there are more cases of ambiguity where the ability of the Scene-Object-Matrix to contribute differentiating information is higher. In addition, the rate of false prediction is higher in these cases, and the reinforcement learning gains new information more rapidly. As a result, the proposed model converges faster and becomes extremely beneficial in cases of platforms with strict low computational resources that dictate shallow CNNs, such as small autonomous systems. Figure 11: Performance on video sources of the VAP model with the VGG16 faster RCNN as the bottom-up classifier. Figure 10: Performance on video sources of the VAP model with the VGG1024 faster RCNN as the bottom-up classifier. As explained, the training dataset of SUN2012 includes mainly objects with low altitude angle view. As a result, when the system started to work it generated low scores for the objects picked up by the video motion detector.