1 October 2012 Depth estimation of features in video frames with improved feature matching technique using Kinect sensor
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Abstract
Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)
Kajal Sharma, Inkyu Moon, Sung Gaun Kim, "Depth estimation of features in video frames with improved feature matching technique using Kinect sensor," Optical Engineering 51(10), 107002 (1 October 2012). https://doi.org/10.1117/1.OE.51.10.107002 . Submission:
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