Multi-dataset Pose Estimation based on the Fusion of Multiple Models
Guanghao Jin, Jieying Wang, Yuqing Wang, Hui Du, Lei Ma, Qingzeng Song · 2024
To increase the accuracy of pose estimation on multiple datasets, this paper proposes a new pose estimation method based on the fusion of multiple models. This method consists of two main steps: firstly, a dataset classification by YoloV5 algorithm is performed that is to classify the dataset that may contain the testing samples; secondly, the key point detection is performed by using a deep learning model-based pose estimation that is trained on the predicted dataset. We evaluated the methods on three publicly datasets: Leeds Sports Pose (LSP) dataset, CrowdPose dataset and AnimalPose one. As the experimental results show, our method can achieve the scalability on multiple datasets while ensuring high accuracy.