What is YOLOv3?
YOLO is similar to CNN. However, there are no flattening layers in YOLOv3. The basic structure of YOLO is:
As you can see in the image above, the goal of YOLO is to generate bounding boxes to detect the position of objects. In the process of YOLO, concatenation occurs, copying the image and joining it before applying preprocessing steps. This concatenation enhances the model’s ability to detect objects. Residual blocks are convolutional layers. One advantage of YOLO is its ability to detect the same object at different sizes, including smaller objects.
Implementation
Object tracking with YOLO : I used darknet to implement it.