Real time multi camera 3D tracking system

Sidorov, Kirill; Hicks, Yulia; Marshall, Daniel; Sanei, Saeid; Chambers, Jonathon A.

doi:10.1049/cp:20061957

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a need in the computer vision community to create data sets with which 3D face tracking systems can be evaluated [9]. This applies both to systems that use multiple cameras [9][10][11][12] or a single camera [13]. We hope that other computer-vision research groups will make use of our capture system and data, and help move forward the research on reliable communication systems for people with disabilities.…”

Section: Introductionmentioning

confidence: 99%

Towards a Multi-camera Mouse-replacement Interface

Magee

Chennamaneni³

et al. 2010

Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems

View full text Add to dashboard Cite

Abstract. We present our efforts towards a multi-camera mouse-replacement system for computer users with severe motion impairments. We have worked with individuals with cerebral palsy or multiple sclerosis who use a publiclyavailable interface that tracks the user's head movements with a single video camera and translates them into mouse pointer coordinates on the screen. To address the problem that the interface can lose track of the user's facial feature due to occlusion or spastic movements, we started to develop a multi-camera interface. Our multi-camera capture system can record synchronized images from multiple cameras and automatically analyze the camera arrangement. We recorded 15 subjects while they were conducting a hands-free interaction experiment. We reconstructed via stereoscopy the three-dimensional movement trajectories of various facial features. Our analysis shows that single-camera interfaces based on twodimensional feature tracking neglect to take into account the substantial feature movement in the third dimension.

show abstract

Section: Introductionmentioning

confidence: 99%

Towards a Multi-camera Mouse-replacement Interface

Magee

Chennamaneni³

et al. 2010

Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems

View full text Add to dashboard Cite

show abstract

“…One of the main advantages of QT is that it generates the same result when it is executed several times; thus it does not suffer from the data ordering/sequence problem. The QT clustering principle has also been used in a 2D face tracking system [18] as well as other micro array analysis [19] and gene expression [17,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

Yaakob

Lim

Jain

2009

Neural Comput & Applic

View full text Add to dashboard Cite

This paper introduces a novel neural network model known as the Euclidean quality threshold ARTMAP (EQTAM) network and its application to pattern classification. The model is constructed based on fuzzy ARTMAP (FAM) and the quality threshold clustering principle. The main objective of EQTAM is to overcome the effects of training data sequences on FAM and, at the same time, to improve its classification performance. Several artificial data sets and benchmark medical data sets are used to evaluate the effectiveness of the proposed model. Performance comparisons between EQTAM and ARTMAPbased as well as other classifiers are made. From the experimental results, it can be observed that EQTAM is able to produce good results. More importantly, the performance of EQTAM is robust against the effect of training data orders or sequences.

show abstract

Real time multi camera 3D tracking system

Cited by 2 publications

References 0 publications

Towards a Multi-camera Mouse-replacement Interface

Towards a Multi-camera Mouse-replacement Interface

A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

Contact Info

Product

Resources

About