## 2 Related Work
In the analysis of clusters stage, the selected clusters are used as a seed point to find the frontal part of a vehicle through the double-stage detection algorithm consisting of a pre-detector, pre-warning, etc. Once vehicle candidates are located, they are classified by the Support Vector Machine (SVM) in the pattern recognition stage. This research reported that the detection accuracy of testing the system was 99% with five false positive detections. The work in [11] proposed a motion-based blind spot detection system based on the optical flow calculation as well. A template matching technique was initially considered to detect and track a vehicle, but this research reported poor performance of the template matching technique due to  
Figure 2: Camera-based blind spot system. a difficulty to capture a motion and shape of the vehicle. The proposed algorithm is composed of several steps: (i) specify a blind spot zone in the grayscale image; (ii) extract features; (iii) estimate the motion vector through calculation of the optical flow; (iv) label valid features as potential features; (v) eliminate noise and finally; and (vi) label vehicle features. The experimental result shows that vehicles were successfully detected and tracked in thirty-eight situations out of forty-one situations without any false positive alarms. The authors of [12] proposed a blind spot monitoring system using the Histogram of Oriented Gradients (HOG) descriptor and SVM. The proposed algorithm introduces four main steps. Once an image frame is captured by a single rear-view camera, the first step is to set the detection window for vehicle detection in either the left or right blind spot area. The second step is to detect vehicles in the detection window using the HOG descriptor and SVM. The authors claimed that the HOG descriptor and SVM showed outstanding performance and reliability. The third step is to estimate the moving direction of the detected vehicles from the previous step. The last step is to generate an alarm signal if vehicles are in the blind spot area. Furthermore, a high-speed algorithm consisting of two additional concepts, i.e., selective filtering of the HOG features and fast estimation of moving direction, is introduced for real-time evaluation with an embedded system. The authors evaluated the developed algorithm with consideration of various environments and vehicles, and measured performance evaluation using recall and precision. The evaluation of the basic algorithm on both urban areas and highways resulted in 98.62% precision and 99.82% recall, respectively. When evaluating the high-speed algorithm, the authors reported 97.26% precision and 99.65% recall, respectively. In [13], the camera-based blind spot detection system in conjunction with a network based on AlexNet was proposed as a real-time embedded system application. In this network structure, there are four blocks after the first convolutional layer, and each block is designed by various combinations of the concepts based on the Visual Geometry Group (VGG), depthwise separable convolutions, residual learning, and the squeeze-and-excitation module. For this reason, four different neural networks are established: (i) only VGG blocks-based network; (ii) network with a combination of depthwise separable convolution and residual learning (Sep-Res); (iii) network with a combination of depthwise separable convolution and the squeeze-and-excitation module (Sep-SE); and (iv) a combination of all three parts (Sep-Res-SE). Two datasets were employed to evaluate each network, and the authors reported that the VGG blocks-based network showed slightly higher test accuracy than other networks, but the Sep-Res-SE network showed better performance in terms of overall evaluation including inference speed and memory cost. Note that the Sep-Res-SE network showed 97.58% detection accuracy. According to the literature research above, there are no doubts that the camera-based blind spot detection system has a potential possibility to be improved.