Robust Head Tracking with Particles Based on Multiple Cues Fusion

2006 ◽  
pp. 29-39 ◽  
Author(s):  
Yuan Li ◽  
Haizhou Ai ◽  
Chang Huang ◽  
Shihong Lao
Keyword(s):  
Head Tracking ◽  
Multiple Cues

Robust Head Tracking Based on Multiple Cues Fusion in the Kernel-Bayesian Framework

2013 ◽  
Vol 23 (7) ◽  
pp. 1197-1208 ◽  
Author(s):  
Xiaoqin Zhang ◽  
Weiming Hu ◽  
Hujun Bao ◽  
Steve Maybank
Keyword(s):  
Bayesian Framework ◽  
Head Tracking ◽  
Multiple Cues

New AAC Access Strategy for Gesture Tracking: A Technical Note

2012 ◽  
Vol 21 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Susan Fager ◽  
Tom Jakobs ◽  
David Beukelman ◽  
Tricia Ternus ◽  
Haylee Schley
Keyword(s):  
Augmentative And Alternative Communication ◽  
Technical Note ◽  
Head Tracking ◽  
Alternative Communication ◽  
Access Methods ◽  
Cord Injury ◽  
Physical Limitations ◽  
Gesture Tracking ◽  
Communication Needs ◽  
Tracking Device

Abstract This article summarizes the design and evaluation of a new augmentative and alternative communication (AAC) interface strategy for people with complex communication needs and severe physical limitations. This strategy combines typing, gesture recognition, and word prediction to input text into AAC software using touchscreen or head movement tracking access methods. Eight individuals with movement limitations due to spinal cord injury, amyotrophic lateral sclerosis, polio, and Guillain Barre syndrome participated in the evaluation of the prototype technology using a head-tracking device. Fourteen typical individuals participated in the evaluation of the prototype using a touchscreen.

Judgments of Learning are Influenced by Multiple Cues in Addition to Memory for Past Test

2011 ◽  
Author(s):  
Jarrod C. Hines ◽  
Christopher Hertzog ◽  
Dayna R. Touron
Keyword(s):  
Judgments Of Learning ◽  
Multiple Cues ◽  
Memory For Past Test

Head tracking latency in virtual environments: Psychophysics and a model

2003 ◽  
Author(s):  
Bernard D. Adelstein ◽  
Thomas G. Lee ◽  
Stephen R. Ellis
Keyword(s):  
Virtual Environments ◽  
Head Tracking

Head Tracking: A Comprehensive Review

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Saket Kumar ◽  
Rajesh Mehra
Keyword(s):  
Head Tracking ◽  
Comprehensive Review

Fast, Reliable Head Tracking Under Varying Illumination

1999 ◽  
Author(s):  
Marco La Cascia ◽  
Stan Sclaroff
Keyword(s):  
Head Tracking

scholarly journals A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

2021 ◽  
Vol 11 (12) ◽  
pp. 5503
Author(s):  
Munkhjargal Gochoo ◽  
Syeda Amna Rizwan ◽  
Yazeed Yasin Ghadi ◽  
Ahmad Jalal ◽  
Kibum Kim
Keyword(s):  
Deep Learning ◽  
Human Head ◽  
Point Clouds ◽  
Head Tracking ◽  
Complex Environments ◽  
People Tracking ◽  
Practical Applications ◽  
Remote Cameras ◽  
Video Frames ◽  
Benchmark Datasets

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

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