Human head tracking using adaptive appearance models with a fixed-viewpoint pan-tilt-zoom camera

2002 ◽  
Author(s):  
K. Yachi ◽  
T. Wada ◽  
T. Matsuyama
Keyword(s):  
Human Head ◽  
Head Tracking ◽  
Appearance Models

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.

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)

ACTIVE HEAD TRACKING BASED ON CHROMATIC SHAPE FITTING

2003 ◽  
Vol 17 (04) ◽  
pp. 529-544
Author(s):  
JASON Z. ZHANG ◽  
Q. M. JONATHAN WU ◽  
WILLIAM A. GRUVER
Keyword(s):  
Real Time ◽  
Relative Motion ◽  
Tracking System ◽  
Human Head ◽  
Head Motion ◽  
Head Tracking ◽  
Functional Modules ◽  
Current Image ◽  
Shape Fitting

This paper presents a method for tracking a human head based on the integration of camera saccade and chromatic shape fitting, which are implemented as functional modules in an active tracking system. Head motion is detected in the saccade module by extracting edges from two successive images. The position of the head in the current image is approximated as the centroid of the apparition formed by the moving edges of the target. A visual position cue is used to drive a pan/tilt camera to perform real-time saccade keeping the target in the foveal area in the image. The shape-fitting module is invoked to extract more information from the target. The shape of the target is modeled as an ellipse whose position, orientation and size are dynamically determined by shape fitting, and implemented with a color registration technique. In the proposed method, quasi real-time pursuit is achieved using a Pentium II computer in an uncontrolled environment with arbitrary relative motion between the target and camera.

scholarly journals AUTOMATIC DETECTION AND TRACKING OF HUMAN HEADS USING AN ACTIVE STEREO VISION SYSTEM

2000 ◽  
Vol 14 (02) ◽  
pp. 137-166 ◽  
Author(s):  
CHENG-YUAN TANG ◽  
ZEN CHEN ◽  
YI-PING HUNG
Keyword(s):  
Template Matching ◽  
Vision System ◽  
Tracking System ◽  
Human Head ◽  
Stereo Image ◽  
Head Tracking ◽  
Detection And Tracking ◽  
Stereo Cameras ◽  
Active Stereo ◽  
Elliptical Model

A new head tracking algorithm for automatically detecting and tracking human heads in complex backgrounds is proposed. By using an elliptical model for the human head, our Maximum Likelihood (ML) head detector can reliably locate human heads in images having complex backgrounds and is relatively insensitive to illumination and rotation of the human heads. Our head detector consists of two channels: the horizontal and the vertical channels. Each channel is implemented by multiscale template matching. Using a hierarchical structure in implementing our head detector, the execution time for detecting the human heads in a 512×512 image is about 0.02 second in a Sparc 20 workstation (not including the time for image acquisition). Based on the ellipse-based ML head detector, we have developed a head tracking method that can monitor the entrance of a person, detect and track the person's head, and then control the stereo cameras to focus their gaze on this person's head. In this method, the ML head detector and the mutually-supported constraint are used to extract the corresponding ellipses in a stereo image pair. To implement a practical and reliable face detection and tracking system, further verification using facial features, such as eyes, mouth and nostrils, may be essential. The 3D position computed from the centers of the two corresponding ellipses is then used for fixation. An active stereo head has been used to perform the experiments and has demonstrated that the proposed approach is feasible and promising for practical uses.

Head Detection by the Adaboost Algorithm for Head Pose Estimation

2013 ◽  
Vol 427-429 ◽  
pp. 1696-1699
Author(s):  
Xiang Yang Liu ◽  
Shao Song Zhu ◽  
Su Qing Wu ◽  
Zhi Wei Shen
Keyword(s):  
Pose Estimation ◽  
Nearest Neighbor ◽  
Human Head ◽  
Head Pose Estimation ◽  
Head Tracking ◽  
Head Pose ◽  
Adaboost Algorithm ◽  
Head Detection ◽  
Dimensionality Reduction Method ◽  
Tracking Module

For human head pose analysis based on videos, pose is usually estimated on the head patch provided by a tracking module. However, head tracking is very sensitive to the large changes of pose. Therefore, this work locates the head patch in the videos by head detection. Firstly, we use the Adaboost algorithm to detect the human head in the video. Secondly, we present a dimensionality reduction method to process the head patch. Finally, we use the nearest neighbor method to estimate the head pose. The experiment results show: accurate head detecting helps to estimate the head pose. This method can be used for complex conditions of accurate head pose estimation.

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