TY  - JOUR
T1  - Video Object Detection Network by Estimation of Center and Movement of The Object by Stacking Continuous Images
AU  - Son, Hayoung 
AU  - Lee, Yujin 
AU  - Choi, Kaewon 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.6.416
KW  - computer vision
KW  - object detection
KW  - video object detection
KW  - deep learning
KW  - anchor-free
AB  - 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.