Growing neural gas for intelligent robot vision with range imaging camera

Sasaki, Hironobu; Fukuda, Toshio; Satomi, Masashi; Kubota, Naoyuki

doi:10.1109/icma.2009.5246241

Cited by 3 publications

(1 citation statement)

References 29 publications

(10 reference statements)

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“…This method start by libelling the head, upper and lower sections in three situation walking, crawling and seated moving in order to track the action. A novel GMM algorithm applying in each libelling area to track the move of body parts in this area (Shi and Malik, 2000;Sasaki et al, 2009;Torralba et al, 2007). To avoid the error of ambiguous object's shape in some frame and object depth in the camera a texture template is used at each previse frame (Kanhere et al, 2005;Sarfraz et al, 2011;Aljuaid et al, 2010;).…”

Section: Jcsmentioning

confidence: 99%

Child Video Dataset Tool to Develop Object Tracking Simulates Babysitter Vision Robot

Aljuaid¹,

Mohamad²

2014

Journal of Computer Science

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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.

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