Object tracking plays an important role in the computer vision field and has many applications such as video surveillance and vehicle navigation. But the occlusion problem is one of the most challenging problems in the applications. Although there are many approaches in the object tracking field that focus on dealing with occlusion scenes, the occlusion with large size barriers and long occlusion time still cannot be solved. To handle the problems, this paper proposes a reliable tracking method based on particle filter focus on long-term full occlusion with large size barriers. In this paper the large size is defined as pixel width from 350 to 600 in fixed resolution images. and the long term is defined as occlusion frame number from 180 to 600. First, this paper proposed a particle position reset module to replace the resampling process during the occlusion periods to solve the problem of losing the target after occlusion. In addition, a hybrid feature based likelihood model is proposed for the occlusion happening and ending judgments. Experiments on the extreme occlusion situation sequences demonstrate the reliability and accuracy of the proposed work on these challenging scenes. The algorithm finally implements the average 92% success rate at the tested sequences.
Successful jumps in figure skating with critical parameters such as proper jump height, spin speed, and the number of jump rotations, which are valuable for analysis in athlete training. Driven by recent computer vision applications, reconstructing 3D poses of the athlete in figure skating to extract the significant variables has become increasingly important. However, a large number of conventional works have obtained 3D poses from corresponding 2D information directly, which ignores the uniqueness of figure skating, such as self-occlusion, abnormal poses, etc. This paper proposes a multi-view voxel based system for calibration and error correction to reconstruct the 3D jumping poses of figure skaters in the presence of 2D heatmaps. The proposed method consists of two key components: Voxels based recovery method of high probability area in 2D heatmap; Plain 2D smoothness and motion trajectory and relative joint positions separable 3D smoothness based rectification method. This work is proven to be applicable to 3D pose dynamics in figure skating jumping motion. Mean Per Joint Position Error (MPJPE) is: 34.58mm in the pre-jump stage, 16.51mm in the jumping stage, 15.73mm in the post-jump stage, and 16.93mm in the whole jump stage, which is 36% improvement compared with the conventional work.
KEYWORDS: 3D acquisition, Machine vision, Computer vision technology, 3D modeling, 3D vision, Video, 3D image processing, Calibration, Pattern recognition, 3D image reconstruction
Driven by recent computer vision applications, recovering 3D pose in the field of figure skating has become increasingly important. However, conventional works have suffered because of getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as restriction from self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multitask architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater in the presence of the 2D Part Confidence Map. The proposals consist of three key components: Temporal smoothness and likelihood distribution based discrete probability points selection; Multi-perspective and combinations unification based large-scale venue 3D reconstruction; Spatial confidence point group and multiple constraints based human skeleton estimation. This work can be applied to 3D animated display and video motion capture of figure skating competition. The accuracy rate on the test sequences is 82.32% in body level and 92.96% in joint level.
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