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Distance Measurement Using the Kinect Sensor with Neuro-image Processing
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 Title & Authors
Distance Measurement Using the Kinect Sensor with Neuro-image Processing
Sharma, Kajal;
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 Abstract
This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.
 Keywords
Kinect sensor;Neural network;Distance estimation;
 Language
English
 Cited by
 References
1.
W. Hu et al., "A survey on visual surveillance of object motion and behaviors," IEEE Transactions on Systems, Man, and Cybernetics, vol. 34, no. 3, pp. 334-352 (2004).

2.
T. L. Liu and H. T. Chen, "Real-time tracking using trust region methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 3, pp. 397-402 (2004). crossref(new window)

3.
J. Fan, X. Shen, and Y. Wu, "Scribble Tracker: A Matting-Based Approach for Robust Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1633-1644 (2012). crossref(new window)

4.
X. Li et al., "Graph mode-based contextual kernels for robust SVM tracking," IEEE International Conference on Computer Vision (ICCV), pp. 1156-1163 (2011).

5.
S. Hare et al., "Efficient online structured output learning for keypoint-based object tracking," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1894-1901 (2012).

6.
M. Grabner, H. Grabner, and H. Bischof, "Learning Features for Tracking," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8 (2007).

7.
D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110 (2004). crossref(new window)

8.
H. Bay et al., "SURF: Speeded Up Robust Features," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359 (2008). crossref(new window)