Abstract
This study presents a Child Video Dataset (CVDS) that has numerous videos of different ages and situation of children. 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 study 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.
Highlights
Body parts tracking from monocular a video sequence has been one of the research areas with an increasing number of technical applications
The aim of this study 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 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
It is mainly solved in controlled situations where several calibrated cameras are used with babysitter robot vision in tracking babies and toddlers
Summary
Body parts tracking from monocular a video sequence has been one of the research areas with an increasing number of technical applications. It is mainly solved in controlled situations where several calibrated cameras are used with babysitter robot vision in tracking babies and toddlers. Processes of an object detection and tracking algorithm occupy object regions across frames (Vedaldi and Soatto, 2006). If upper part of a baby is the visible part, only the upper part is labeled as object This attention is essential to allow the detection and tracking algorithm to measure the performance of multiple moving objects more correctly
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