## 4 Experiments with Record Video Images  
Prior to conducting experiments, we came up with two possible scenarios. The first scenario is when the system-equipped vehicle stops or moves more slowly than a target vehicle. The second scenario is when the system-equipped vehicle moves more quickly than a target vehicle. Based on these, the research framework was applied to two video images recorded using the set up in Figure 4. Four vehicles and two vehicles entered a blind spot in the first and second scenarios, respectively. As mentioned above, the research framework was implemented in open source software on the cloud platform. Overall, 28.89 s were taken to extract HOG features, and 461 frames were captured in the first scenario. In addition, in total, 386 frames were captured in the second scenario. In the first experiments with the first scenario, we encountered false positive issues. Here, the meaning of false positive is that, despite the object not being associated with a vehicle, it was captured as a vehicle feature. Most false positive issues were caused by a vehicle shadow. To solve such issues, we analyzed the whole source code again for the purpose of root cause analysis and realized that the causes resulted from hyperparameters. Note that hyperparameters this time refer to the size of a rectangle box, threshold value, and memory size, and these are different from the hyperparameters of the deep learning model, as mentioned in Section 3.4. The identified solution with respect to the size of a rectangle box was that, once it was increased, false positives were reduced. For this reason, the size of a rectangle box was maximized to 9000. As described above, the initial threshold value was 10. This value was reduced to 2, but the problem was that, while performance was improved, false positives were increased. Hence, the threshold value did not change for testing on the cloud platform. The concept of detection memory is that it is used to remember which pixels presented vehicles in previous frames. Since the system detects a vehicle based on vehicle features, detection memory helps the system remember a vehicle in a blind spot. Hyperparameters were tuned several times, and the best combinations used for a proof of concept on the cloud platform were as follows:  
* the size of a rectangle box: 9000
* the threshold value: 10  
Figure 9: Overall research framework. A total of 2000 vehicle images and approximately 9000 non-vehicle images were extracted for the purpose of data preprocessing. The HOG descriptor was applied for feature extraction, and 1188 HOG features for both vehicle and non-vehicle objects were fed into the FCN model as inputs. Keep in mind that representation or feature learning was not applied in this research due to hardware constraints. Two hidden layers, \(1\times 10^{-6}\) learning rate, and 10 epochs with Adam optimizer and ReLus activation were employed for training and testing the FCN model, and the training and testing accuracies achieved were 99.4% and 99.0%, respectively. We set a blind spot to a range of 400–800 px in x-axis and 450–550 px in y-axis in the 1300 px \(\times\) 800 px image and applied the sliding window technique to detect vehicle features. Finally, the heat map and thresholding were employed to reduce false positive errors.  
* the size of detection memory: 4  
Figure 10 depicts experimental results of each scenario, and we confirmed that vehicles in the recorded video images were correctly detected without false positives. Note that the vehicle license plate and driver face were shaded in gray for privacy reasons.