Tracking feature extraction techniques with improved SIFT for video identification
Tóm tắt
This paper presents a method for tracking of object movements and detecting of feature to identify video content using improved Scale-Invariant Feature Transform (SIFT). SIFT can robustly identify objects even among clutter and under partial occlusion, because the SIFT feature descriptor is invariant to uniform scaling, orientation, and also partially invariant to affine distortion and illumination changes. Even if the video drops frames or attacked, our method can extract the features. In our method we detect the video features from tracking the object’s movement and make a dataset with feature sequences to identify video. In contrast to the existing tracking techniques, our method recognized reliable object coordinate. The developed algorithm will be an essential part of a completely tracking and identification system. To evaluate the performance of the proposed approach, we was experimenting with several genres of video. Compare with the original SIFT algorithm, we reducing up to 5 % in processing time was achieved for matching. Also appoint the position of the object area in tracking method make the proposed method automatic, fast and effective.
Tài liệu tham khảo
Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features, Proceedings of the ninth European Conference on Computer Vision, May 2006
Ce L, Jenny Y, Antonio T (2011) SIFT flow: dense correspondence across scenes and its applications, IEEE Transactions on Pattern Analysis and “Machine Intelligence”, 33(5)
Chapelle O, Schokopf B, Zien A (2006) Semi-surpervised learning. MIT Press, Cambridge
Horn BKP, Schunk BG (1981) Determining optical flow. Artif Intell 17:185–203
Jin R, Kim J (2012) A digital watermarking scheme using hologram quantization, SIP2012 342:39–46
Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors, Computer Vision and Pattern Recognition
Kim J, Kim N, Lee D, Park S, Lee S (2010) Watermarking two dimensional data object identifier for authenticated distribution of digital multimedia contents. Signal Process Image Commun 25:559–576
Lee Y, Kim J (2011) Robust blind watermarking scheme for digital images based on discrete fractional random transform. Commun Comput Inf Sci 263(139145)
Li D, Kim J (2012) Secure image forensic marking algorithm using 2D barcode and off-axis Hologram in DWT-DFRNT domain. Appl Math Inf Sci (AMIS) 6(2S):513–520
Lowe DG (1999) Object Recognition from local scale-invariant features. International Conference on Computer Vision, Corfu, Greece (Sep. 1999), 1150–1157
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision, In Proceedings of the International Joint Conference on Artificial Intelligence, 674–679
Zdennek K, Krystian M, Jiri M. Tracking-learning detection
Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3:1–130