Object Identification and Tracking Using YOLO Model: A CNN-Based Approach

The object identification, detection and tracking them in the individual video frames are an expensive and highly recommended task for security and surveillance. This work expects to consolidate the procedure for object recognition with the objective of accomplishing high precision with a real-time performance. A significant test in huge numbers of the object detection frameworks is the reliance on other computer vision systems for helping the profound learning-based methodology, which prompts moderate and nonideal execution. A deep learning-based approach will be used to solve the problem of object identification in an end-to-end fashion. The framework is set up on the most testing straightforwardly open dataset (PASCAL VOC), on which an article revelation challenge is driven each year. The subsequent framework is quick and exact, along these lines helping those applications which require object detection. This work also demonstrates an appropriate study for well popular methods.