Video Object Detection Network by Estimation of Center and Movement of The Object by Stacking Continuous Images

Hayoung Son, Yujin Lee, Kaewon Choi

http://doi.org/10.5626/JOK.2022.49.6.416

Various obstacles such as large containers and logistics machines are placed, in an environment such as a spacious port that is difficult to monitor at once. We studied object detection methods to track very small pedestrians and port vehicle objects. Since we need to learn small objects and unclear shapes, we trained a model based on CenterNet, a network of Anchor-Free methods, and to supplement information on very small objects, we learned by stacking several consecutive images. In addition, Lack of datasets due to the special environment was solved by enhancing data that uses multiple datasets together, randomly selecting multiple still images, and processing them into a continuous image, thereby preventing overfitting.