Name
Abstract | ||
|---|---|---|
Numerous image-processing technologies are essential in order to recognize an object. Object detection depends on the time-sequence of the video frames. Furthermore, manifold object tracking should be done in the line of the computer's vision. To simulate a babysitter's vision, our application was developed to track objects in a scene with the main goal of creating a reliable and operative moving child-object detection system. The aim of this paper is to explore novel algorithms to track a child-object in an indoor and outdoor background video. It focuses on tracking a whole child-object while simultaneously tracking the body parts of that object to produce a positive system. This effort suggests an approach for labeling three body sections, i.e., the head, upper, and lower sections, and then for detecting a specific area within the three sections, and tracking this section using a Gaussian mixture model (GMM) algorithm according to the labeling technique. The system is applied in three situations: child-object walking, crawling, and seated moving. During system experimentation, walking object tracking provided the best performance, achieving 91.932% for body-part tracking and 96.235% for whole-object tracking. Crawling object tracking achieved 90.832% for body-part tracking and 96.231% for whole-object tracking. Finally, seated-moving-object tracking achieved 89.7% for body-part tracking and 93.4% for whole-object tracking. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SOCPAR.2013.7054101 | Soft Computing and Pattern Recognition |
Keywords | Field | DocType |
Gaussian processes,humanoid robots,image motion analysis,mixture models,object detection,object tracking,robot vision,video signal processing,GMM,Gaussian mixture model,babysitter vision robot,body-part tracking,child-object walking,computer vision,crawling object tracking,image-processing technology,labeling technique,moving child-object detection system,seated moving,time-sequence,video frame,whole-object trackin,GMM,Object tracking,babysitter robot vision,body-part tracking,computer vision,robot vision | Computer vision,Object detection,Robot vision,Crawling,Pattern recognition,Computer science,Tracking system,Video tracking,Artificial intelligence,Robot,Mixture model | Conference |
ISBN | Citations | PageRank |
978-1-4799-3399-0 | 0 | 0.34 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanan Aljuaid | 1 | 11 | 1.69 |
| Dzulkifli Mohamad | 2 | 96 | 13.41 |