## 2 Related Work
### Previous Works  
A lot of studies have already been conducted on vehicle detection and tracking. Significant advancements have been made in computer vision and machine learning to solve this issue, leading to the development of various algorithms. The expansion and enhancement of automated driving and computer vision technologies have demonstrated the rising importance of detection and tracking in the study area. The research techniques of vehicle detection are classified into three major categories: feature-based, conventional-based training, and deep learning. The feature-based detection technique generally identifies vehicles based on their prominent look. [PERSON] et al. [44] applied a method for front vehicles using sensor images. Furthermore, this method has improved the region's edges and then transmitted them for confirmation to the support vector machine (SVM) classifier. However, this technique was not able to produce better results compared to other state-of-the-art algorithms when it came to the viewing angle and natural environment. Therefore, this technique applies to roads. [PERSON] et al. [45] employed a segmentation technique of the iterative method to find the shadow characteristics for the vehicle, utilized specific masks to acquire object characteristics at different ranges, and used the restrictions of vanishing points to obtain the result for front vehicles with speed. However, these feature-based methods achieve a high detection rate under different conditions with low algorithm complexity. The conventional detection methods based on machine learning use feature classifiers to identify the vehicles. [PERSON] et al. [46] used a method for segmenting the color space to scan the image with a combination of multi-dimensional Haar-like features and the Adaboost algorithm. [PERSON] et al. [47] used the histogram of oriented gradients (HOGs), which has efficiently minimized the total computation and accelerated the vehicle identification. [PERSON], [PERSON] et al. [48] utilized a Haar-like local binary pattern (LBP) and HOG fusion classification algorithm for object tracking and achieved a better detection rate. With the recent rapid advances in machine learning, CNNs that use deep learning represent a popular and promising area for computer vision. This technique has successfully been applied to classify images, speech recognition, and processing natural languages. Computational intelligence detection generally employs deep CNN to mechanically extract the characteristics of vehicles' objects. And, the vehicle identification task is afterwards eventually categorized to significantly decrease the computation time for such a method and eventually obtain good accuracy with routing protocol optimization. [PERSON] et al. [PERSON] et al. (2018) proposed a multi-scale SPP approach for vehicle detection, which would acquire the input images' aspects of various dimensions. [PERSON] et al. [PERSON] et al. (2019) proposed a method based on detection in two-stage to achieve better segmentation results during the first phase. The second phase uses multiple linear preservation networks to retrieve the geographical configurations of elements in the region of interest (ROI). YOLO and Deep SORT are used together in many MOT applications such as fruit detection [PERSON] et al. (2019), traffic [PERSON] et al. (2019), crowd control [PERSON] et al. (2019), and obstacle detection and tracking [PERSON] et al. (2019). Object tracking deals with the motion of an object frame by frame. Single-object tracking (SOT) and multiple object tracking (MOT) are the two types of objects tracking Kalman filtering, and particle filtering is based on SOT. Deep SORT and SORT are two state-of-the-art MOT-based algorithms. [PERSON], [PERSON] et al. [PERSON] et al. (2019) introduced Deep SORT for virtual lines and multi-vehicle tracking. However, the vehicle movement direction was not considered to obtain the traffic flow.