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.

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 Development and Application of the Stereo Vision Tracking System with Virtual Reality

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Chia-Sui Wang ◽  
Ko-Chun Chen ◽  
Tsung Han Lee ◽  
Kuei-Shu Hsu
Keyword(s):  
Virtual Reality ◽  
Stereo Vision ◽  
Error Rate ◽  
Vision System ◽  
Tracking System ◽  
Tracking Error ◽  
Stereo Image ◽  
Image Tracking ◽  
Stereo Vision System ◽  
Image Depth

A virtual reality (VR) driver tracking verification system is created, of which the application to stereo image tracking and positioning accuracy is researched in depth. In the research, the feature that the stereo vision system has image depth is utilized to improve the error rate of image tracking and image measurement. In a VR scenario, the function collecting behavioral data of driver was tested. By means of VR, racing operation is simulated and environmental (special weathers such as raining and snowing) and artificial (such as sudden crossing road by pedestrians, appearing of vehicles from dead angles, roadblock) variables are added as the base for system implementation. In addition, the implementation is performed with human factors engineered according to sudden conditions that may happen easily in driving. From experimental results, it proves that the stereo vision system created by the research has an image depth recognition error rate within 0.011%. The image tracking error rate may be smaller than 2.5%. In the research, the image recognition function of stereo vision is utilized to accomplish the data collection of driver tracking detection. In addition, the environmental conditions of different simulated real scenarios may also be created through VR.

HUMAN FALL DETECTION AND TRACKING SYSTEM USING MEMS SENSOR

2017 ◽  
Vol 6 (3) ◽  
pp. 20
Author(s):  
A. SAIPRIYA ◽  
V. MEENA ◽  
MAALIK M.ABDUL ◽  
D. PRAVINRAJ ◽  
P. JEGADEESHWARI ◽  
...  
Keyword(s):  
Tracking System ◽  
Fall Detection ◽  
Mems Sensor ◽  
Detection And Tracking

scholarly journals An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hong-Min Zhu ◽  
Chi-Man Pun
Keyword(s):  
Template Matching ◽  
Tracking System ◽  
Recognition System ◽  
Appearance Model ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Motion Trajectory ◽  
Hand Motion ◽  
Target Hand ◽  
Gesture Tracking

We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.

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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