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.

Multisensor Integrated Prediction for Virtual Reality

1998 ◽  
Vol 7 (4) ◽  
pp. 410-422 ◽  
Author(s):  
Satoru Emura ◽  
Susumu Tachi
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Rotational Motion ◽  
Sampling Rate ◽  
Human Head ◽  
Head Motion ◽  
Boundary Process ◽  
Rms Error ◽  
Magnetic Tracker ◽  
Quaternion Representation

Unconstrained measurement of human head motion is essential for HMDs (headmounted displays) to be really interactive. Polhemus sensors developed for that purpose have deficiencies of critical latency and low sampling rates. Adding to this, a delay for rendering virtual scenes is inevitable. This paper proposes methods that compensate the latency and raises the effective sampling rate by integrating Polhemus and gyro sensors. The adoption of quaternion representation enables us to avoid singularity and the complicated boundary process of rotational motion. The ability of proposed methods under various rendering delays was evaluated in the respect of RMS error and our new correlational technique, which enables us to check the latency and fidelity of a magnetic tracker, and to assess the environment where the magnetic tracker is used. The real-time implementation of our simpler method on personal computers is also reported in detail.

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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