## 2 Related Work
### YOLO-Based Methods
Their strength in real-time detection and classification of objects in videos made them useful in several applications. The YOLOv2 is very efficient at detecting and classifying simple objects. The GPU features and the Anchor Box approach were used to accomplish the desired speed and precision. Furthermore, YOLOv2 can accurately detect object movement in video recordings. YOLO9000 is a real-time framework that can maximize detection and classification while also bridging the gap. The YOLOv2 model and the YOLO 9000 detection system can detect and classify a wide range of items, from multiple occurrences of a single object to multiple instances of various objects. In [24], an improved YOLOv4-based video stream vehicle target detection system was used to address the problem of slow detection speed. This study first presents a theoretical overview of the YOLOv4 algorithm, then offers an algorithmic technique for increasing detection speed, and lastly conducts real road tests. According to the experimental results, the algorithm in this work can improve detection speed without sacrificing accuracy, which can be used to make decisions for safe vehicle driving. In [25], the YOLOv5 is used to locate weighty supplies vehicles during cold weather and thus allowed the prediction of parking place slots in real-time. The authors employ infrared network cameras, since snowy conditions and the polar night in the winter pose certain obstacles for image recognition. Authors used the YOLOv5 to analyze if the front cabin and back are adequate features to identify heavy goods vehicles because these photos repeatedly have large overlaps. The trained algorithm reliably distinguishes the front of heavy goods vehicles. However, detecting the back cabin appears to be more difficult, especially when the vehicle is placed far away from the camera. Finally, they show that detecting heavy goods vehicles utilizing their front and rear instead of the entire vehicle improves detection during winters, which mostly experience difficult images with significant objects overlaps and cut-offs. Recently, some of the learning-based approaches [26] and the CNN based methods [27] also report encouraging results in the vehicle detection domain. In [26], authors developed a box-free instance segmentation method using semi-supervised iterative learning. The iterative learning procedure considered labeling vehicles from the entire scene and then trained the deep learning model for classification. Authors also considered vehicle interiors and borders to isolate instances using a semantic segmentation. In [27], researchers performed a fully convolutional regression network. In this method the training stage uses an input image along with its ground to describe each vehicle as a 2-D Gaussian function distribution. Hence, the vehicle's original format attains a simplified elliptical shape in the ground truth and output images. The vehicle segmentation uses a fixed threshold in the predicted density map to generate a binary mask. This method prevents grouping cars and favors counting. Moreover, vehicles take on a different form that is expressed by the Gaussian function. In [28], a robust vehicle detection model is developed, which is referred to as YOLOv4_AF. This model introduces an attention mechanism that suppresses the interference features of images through channel length and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN) is also applied to enhance the effective features. This way, the objects are steadily positioned in the 3D space that ultimately improves the vehicle object detection and classification performance. In [29], vehicle detection and tracking are achieved through a multi-scale deep convolution neural network. This work also applies conventional Gaussian mixture probability hypothesis along with hierarchical data association that divides into detection-to-track and track-to-track associations.