Vertical and Horizontal Network for Small Object Detection in Sports Videos
- Resource Type
- Conference
- Authors
- Han, Xiao; Wang, Yongbin; Wang, Qi; Zhang, Nenghuan; Liu, Chenming
- Source
- 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2024 IEEE 7th. 7:1879-1884 Mar, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Automation
Fuses
Neural networks
Object detection
Games
Feature extraction
Task analysis
small object detection
ball detection
sports video analysis
- Language
- ISSN
- 2689-6621
Detecting small objects is often impeded by blurriness and low resolution, which poses substantial challenges for accurately detecting and localizing such objects. Balls and players in sports competition videos are small objects more difficult to detect because of motion blur. Balls and players in sports game videos are small objects that are more difficult to detect due to motion blur. To tackle this challenge, we propose a small object detection backbone network named VHNet based on vertical and horizontal information flow in sports scenarios. In order to verify the effectiveness of our backbone network, we manually annotated a small ball detection data set and conducted comparative experiments with the SOTA small object detection method. The results demonstrate the superiority of our method in sports video detection tasks.