Object Detection Classification and Tracking of Everyday Common Objects

This project presents an advanced computer vision system for object detection, classification, and tracking utilizing the cutting-edge YOLOv4 algorithm. Recent advances in deep learning have led to significant improvements in the accuracy and speed of object detection models. The project focuses on training the YOLOv4 model on large-scale datasets with diverse object categories. By employing transfer learning techniques, the model will be fine-tuned to adapt to specific target objects of interest, achieving a high level of accuracy and generalization.The Object detection, classification and tracking model achieves high accuracy in detecting and tracking objects. The performance analysis of the system showcases promising results.The fluctuation results due to the model not being very robust to occlusions. Overall, the model significantly improves the accuracy of existing model by detecting the targets that are very close to the edges of the frame to by focusing on them before they exit the frame. The model counts the objects and get their position information when tacking. However, ongoing improvement efforts are necessary to address potential challenges, such as real time multi object tracking, object association and occlusion handling