scholarly journals Grid Particle Filter for Human Head Tracking Using 3D Model

2008 ◽  
Author(s):  
Chenguang Liu ◽  
Jiafeng Liu ◽  
Jianhua Huang ◽  
Xianglong Tang
Keyword(s):  
Particle Filter ◽  
3D Model ◽  
Human Head ◽  
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.

Modeling head tracking of visual targets

2002 ◽  
Vol 12 (1) ◽  
pp. 25-33
Author(s):  
K.J. Chen ◽  
E.A. Keshner ◽  
B.W. Peterson ◽  
T.C. Hain
Keyword(s):  
Control System ◽  
Visual Feedback ◽  
Human Head ◽  
Feedback System ◽  
Optimization Methods ◽  
Head Tracking ◽  
Feedback Systems ◽  
Control Gain ◽  
Head Control ◽  
Visual Targets

Control of the head involves somatosensory, vestibular, and visual feedback. The dynamics of these three feedback systems must be identified in order to gain a greater understanding of the head control system. We have completed one step in the development of a head control model by identifying the dynamics of the visual feedback system. A mathematical model of human head tracking of visual targets in the horizontal plane was fit to experimental data from seven subjects performing a visual head tracking task. The model incorporates components based on the underlying physiology of the head control system. Using optimization methods, we were able to identify neural processing delay, visual control gain, and neck viscosity parameters in each experimental subject.

scholarly journals Depth and thermal information fusion for head tracking using particle filter in a fall detection context

2020 ◽  
Vol 27 (2) ◽  
pp. 195-208
Author(s):  
Imen Halima ◽  
Jean-Marc Laferté ◽  
Geoffroy Cormier ◽  
Alain-Jérôme Fougères ◽  
Jean-Louis Dillenseger
Keyword(s):  
Particle Filter ◽  
Information Fusion ◽  
Fall Detection ◽  
Head Tracking

Mechanisms controlling human head stabilization. I. Head-neck dynamics during random rotations in the horizontal plane

1995 ◽  
Vol 73 (6) ◽  
pp. 2293-2301 ◽  
Author(s):  
F. A. Keshner ◽  
B. W. Peterson
Keyword(s):  
Visual Feedback ◽  
Horizontal Plane ◽  
Mental Arithmetic ◽  
Human Head ◽  
The Body ◽  
Myoelectric Activity ◽  
Head Tracking ◽  
Arithmetic Task ◽  
Head Velocity ◽  
Head Neck

1. Potential mechanisms for controlling stabilization of the head and neck include voluntary movements, vestibular (VCR) and proprioceptive (CCR) neck reflexes, and system mechanics. In this study we have tested the hypothesis that the relative importance of those mechanisms in producing compensatory actions of the head-neck motor system depends on the frequency of an externally applied perturbation. Angular velocity of the head with respect to the trunk (neck) and myoelectric activity of three neck muscles were recorded in seven seated subjects during pseudorandom rotations of the trunk in the horizontal plane. Subjects were externally perturbed with a random sum-of-sines stimulus at frequencies ranging from 0.185 to 4.11 Hz. Four instructional sets were presented. Voluntary mechanisms were examined by having the subjects actively stabilize the head in the presence of visual feedback as the body was rotated (VS). Visual feedback was then removed, and the subjects attempted to stabilize the head in the dark as the body was rotated (NV). Reflex mechanisms were examined when subjects performed a mental arithmetic task during body rotations in the dark (MA). Finally, subjects performed a voluntary head tracking task while the body was kept stationary (VT). 2. Gains and phases of head velocity indicated good compensation to the stimulus in VS and NV at frequencies < 1 Hz. Gains dropped and phases advanced between 1 and 2 Hz, suggesting interference between neural and mechanical components. Above 3 Hz, the gains of head velocity increased steeply and exceeded unity, suggesting the emergence of mechanical resonance.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals 3D Head Tracking using the Particle Filter with Cascaded Classifiers

2006 ◽  
Author(s):  
Y. Kobayashi ◽  
D. Sugimura ◽  
K. Hirasawa ◽  
N. Suzuki ◽  
H. Kage ◽  
...  
Keyword(s):  
Particle Filter ◽  
Head Tracking ◽  
Cascaded Classifiers

Multiple-Person Tracking by Multiple Cameras and Laser Range Scanners in Indoor Environments

2010 ◽  
Vol 22 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Hiroshi Noguchi ◽  
◽  
Taketoshi Mori ◽  
Takashi Matsumoto ◽  
Masamichi Shimosaka ◽  
...  
Keyword(s):  
Particle Filter ◽  
Human Body ◽  
Body Position ◽  
Human Head ◽  
Specific Information ◽  
Indoor Environments ◽  
Multiple Cameras ◽  
Laser Range ◽  
Person Tracking ◽  
Weak Points

In this paper, we propose a method for multiple-person tracking using cameras and laser range scanners. Our method estimates 3D positions of human body and head, and labels them with their identities. Individual particle filters track person correctly by integrating information from laser range scanners and target-specific information from cameras, thus compensating for weak points of each. We also develop a particle filter framework that tracks the human head simultaneously using the estimated body position. Results of experiments demonstrate the effectiveness and robustness of the proposal in tracking multiple persons with multiple scanners and cameras.

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