A Tracking Framework for Collaborative Human Computer Interaction

Authors:
E. Polat
Affiliations:
-
Venue:
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Year:
2002

Citing 5
Cited 1

Finding Trajectories of Feature Points in a Monocular Image Sequence

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hydra: Multiple People Detection and Tracking Using Silhouettes

VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance

Tracking body parts of multiple people for multi-person multimodal interface

ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

The ability to track multiple people and their body parts (i.e., face and hands) in a complex environment is crucial for designing a collaborative natural human computer interaction (HCI). One of the challenging issues in tracking body parts of people is the data association uncertainty while assigning measurements to the proper tracks in the case of occlusion and close interaction of body parts of different people. This paper describes a framework for tracking body parts of people in 2D/3D using multiple hypothesis tracking (MHT) algorithm. A path coherence function has been incorporated along with MHT to reduce the negative effects of closely spaced measurements that produce unconvincing tracks and needless computations. The performance of the framework has been validated using experiments on real sequence of images.