01843nas a2200241   4500000000100000000000100001008004100002260001200043653002200055653003700077653002400114653002000138653001800158100001300176700001200189700001200201245006500213856009600278300001200374490000600386520119500392022001401587       2019                            d  c03/201910aAugmented Reality10aHuman-Computer Interaction (HCI)10aGesture Recognition10aVirtual Reality10a3D Navigation1 aI Rehman1 aS Ullah1 aM Raees00aTwo Hand Gesture Based 3D Navigation in Virtual Environments  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/07/ijimai_5_4_15_pdf_17617.pdf  a128-1400 v53 aNatural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec.  a1989-1660