Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions
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 th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9164977/ |
| _version_ | 1818566742170927104 |
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| author | Dongnian Li Yang Guo Chengjun Chen Zhengxu Zhao |
| author_facet | Dongnian Li Yang Guo Chengjun Chen Zhengxu Zhao |
| author_sort | Dongnian Li |
| collection | DOAJ |
| description | 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 hand-object 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. |
| first_indexed | 2024-12-14T01:57:31Z |
| format | Article |
| id | doaj.art-4ee4f323b207460eba33731c93b74d1a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T01:57:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ee4f323b207460eba33731c93b74d1a2022-12-21T23:21:07ZengIEEEIEEE Access2169-35362020-01-01814828914830010.1109/ACCESS.2020.30158349164977Collaborative Differential Evolution Filtering for Tracking Hand-Object InteractionsDongnian Li0https://orcid.org/0000-0003-0327-5205Yang Guo1https://orcid.org/0000-0002-6111-8438Chengjun Chen2https://orcid.org/0000-0003-3185-1062Zhengxu Zhao3https://orcid.org/0000-0002-6066-623XSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, ChinaHuman 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 hand-object 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.https://ieeexplore.ieee.org/document/9164977/Differential evolutiondepth imagehand trackingobject trackingparticle filtering |
| spellingShingle | Dongnian Li Yang Guo Chengjun Chen Zhengxu Zhao Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions IEEE Access Differential evolution depth image hand tracking object tracking particle filtering |
| title | Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions |
| title_full | Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions |
| title_fullStr | Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions |
| title_full_unstemmed | Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions |
| title_short | Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions |
| title_sort | collaborative differential evolution filtering for tracking hand object interactions |
| topic | Differential evolution depth image hand tracking object tracking particle filtering |
| url | https://ieeexplore.ieee.org/document/9164977/ |
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