Performance analysis of object detection algorithms on youtube video object dataset

Object Recognition is a terminology used to refer to a collection of computer vision tasks that are involved in object identification in digital images and videos. In this paper, different object detection algorithms were implemented on Youtube object dataset. Each object detection algorithm has its own advantages and limitations which depend on the dataset used. It was observed that YOLO and SSD, being state-of-art algorithms, demonstrate better performance than other models on youtube video object dataset. SSD is better at detecting smaller objects. Centernet performs poorly on this dataset.