TinySeeker: A Network for Seeking Tiny and Fast Moving Object Based on Asymmetric U-Net
Yu-Chun Lo, Po‐Yung Chou, Bo-Zheng Xie, Cheng-Hung Lin · 2023
To refine strategies and augment skills, both professional athletes and amateur players routinely utilize cameras to document their practice sessions and games. As a result, an increasing number of researchers are exploring this field, aiming to offer comprehensive insights. Object detection is a pivotal task within this field, as identifying object locations can provide valuable insights, such as strategic analysis. However, only a limited number of studies have specifically focused on tracking fast-moving and indistinct objects such as a badminton shuttlecock. The preceding method, TrackNetv2, proposed the use of VGG-16 and U-Net, a heatmap-based approach, for badminton detection. However, the architecture of U-Net demands substantial computational resources in this paper. To tackle this issue, we present a pioneering asymmetric architecture named Tinyseeker inspired by U-Net. This novel model not only assures precise detection of the badminton shuttlecock's location, but it also champions computational efficiency. The reimagined structure strikes an optimal balance between detection accuracy and computational demands, making it a practical and effective solution for real-world applications. Experimental results show that Tinyseeker can reduce calculation up to 26% while remaining the precision. This architecture marks a significant advancement in the field, pushing the boundaries of what is possible within object detection tasks and setting a new benchmark for similar studies in the future.