Object tracking simulates babysitter vision robot using GMM

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.