A method for missing joint recovery and its application in skeleton-based activity recognition

The recognition of human activities is a crucial problem in computer vision, aiming to determine the activities of individuals or groups based on a sequence of images or videos. While previous studies have demonstrated feasible results in activity recognition from skeletal joints, challenges arise in conditions with occlusion, complex motions, and inaccurate joint estimations, leading to missing joints that can impact the effectiveness of subsequent recognition steps. This paper proposes a method for recovering missing joints in the estimation of human joints from motion in videos and applying it to activity recognition. With the proposed method, the results of the model’s 3D space estimation with missing joints show great potential, although the model still exhibits some weaknesses in challenging scenarios. For the activity recognition task, the recognition results of the DD-Net model on the Mica-Action3D dataset have improved by up to 25%.