Skeleton body pose tracking from efficient three-dimensional motion estimation and volumetric reconstruction

Zhang, Zheng; Soon, Hock Wei

doi:10.1364/ao.51.005686

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments are conducted on the two real sequences to evaluate the proposed CoDEF algorithm. We compare CoDEF with two algorithms: another improved PF algorithm with DE operators (DEPF) [46] and a hybrid particle-based search (HPS) algorithm that derives from PSO and DE [33]. Both DEPF and HPS track in the hand-object joint pose space.…”

Section: A Experiments On Real Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Guo

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Human hands engage in interactive activities in many practical working scenarios, among which the interactions between human hands and objects are the most common. Tracking the movement of the human hand during hand-object interactions is an important research task that is also challenging due to the high-dimensionality and occlusions. In this paper, we track hand-object interactions from depth observations with a model-based method. To overcome the difficulties of optimum searching in the handobject high-dimensional space, we propose a new algorithm -collaborative differential evolution filtering (CoDEF) -for tracking hand-object interactions. The proposed CoDEF algorithm integrates the differential evolution (DE) algorithm into a particle filtering (PF) framework to accelerate the convergence of particles. Particles are driven to the regions with a high probability by optimizing the matching error under the current observation with DE. To decompose the state space and decrease the complexity of optimum searching, CoDEF tracks the movement of the hand and object by using two collaborative trackers. Based on the proposed CoDEF algorithm, we develop a model-based tracking system with 3D graphic techniques. According to the experimental results, the proposed CoDEF algorithm can achieve robust tracking of hand-object interactions using fewer particles.INDEX TERMS differential evolution, depth image, hand tracking, object tracking, particle filtering

show abstract

Section: A Experiments On Real Imagesmentioning

confidence: 99%

“…They deduced the object pose according to the hand pose and the hand-object interaction model. Zhang et al [33] performed a hybrid particle-based search that derives from PSO and differential evolution (DE) to track human body poses. They used a voxel model for the human body.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Guo

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

SIFT flow for large-displacement object tracking

Zhang

2014

Appl. Opt.

View full text Add to dashboard Cite

Traditional tracking methods place an emphasis on how to cope with the variations in target appearance effectively. However, when the motion displacement of the target between image frames becomes larger, these methods may be unstable. This paper presents a novel (to our knowledge) visual object tracking method. In this method, we first introduce scale-invariant feature transform (SIFT) flow into the tracking problem and develop a real-time motion prediction method to capture large displacement between consecutive image frames. Then we use belief propagation (BP) to convert the problem of finding maximum a posteriori probability (MAP) to globally minimizing an energy function to get the best matching pairs of points for producing good candidate regions of the target. And last, the refined point trajectories are obtained according to the bidirectional flow field consistency estimation and covariance region descriptor matching, which can update model states efficiently so as to achieve enhanced robustness for visual tracking. Compared with the state-of-art tracking methods, the experimental results demonstrate that the proposed algorithm shows favorable performance when the object undergoes large motion displacement between image frames.

show abstract

Skeleton body pose tracking from efficient three-dimensional motion estimation and volumetric reconstruction

Cited by 2 publications

References 26 publications

Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

SIFT flow for large-displacement object tracking

Contact Info

Product

Resources

About