The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper, Principal Component Analysis (PCA) is applied to the 64-Dimension (D) Speeded Up Robust Features (SURF) descriptor to reduce the descriptor dimensionality and computational time, and suggest the minimum number of dimensions needed for reliable tracking with the Kalman Filter (KF). Tests using image sequences, from an RGB-D camera, are used to validate the performance of the reduced PCA-SURF descriptors as compared to the standard SURF descriptor.
Reference:
Pancham, A, Withey, D.J. and Bright, G. 2015. Tracking image features with PCA-SURF descriptors. In: MVA2015 IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN
Pancham, A., Withey, D. J., & Bright, G. (2015). Tracking image features with PCA-SURF descriptors. IEEE Xplore. http://hdl.handle.net/10204/8149
Pancham, A, Daniel J Withey, and G Bright. "Tracking image features with PCA-SURF descriptors." (2015): http://hdl.handle.net/10204/8149
Pancham A, Withey DJ, Bright G, Tracking image features with PCA-SURF descriptors; IEEE Xplore; 2015. http://hdl.handle.net/10204/8149 .
MVA2015 IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.
