A visual tracking method is proposed to solve the problem that the local feature model is prone to be influenced by the deformation and feature mismatch and leads to drifting. The method constrains the Hough local model by using global features. The Hough model is constructed with a set of local features of an object and estimates the stability of each local feature based on the generalized Hough Transform. Then
the model is updated flexibly through modifying the local feature set to adapt to the object appearance variations. Color cues of the local features are used to calculate the global color probability of being foreground or background. Then the global probability is used to adjust the tracking results and constrains the local features of the model in return. Experimental results on several public video sequences show the robustness of the proposed method in tracking objects with deformation and partial occlusion.
关键词
Keywords
references
BABENKO B, YANG M H, BELONGIE S. Visual tracking with online multiple instances learning[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ, USA: IEEE, 2009: 983-990.
ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1/2/3): 125-141.
KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking learning detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
GODEC M, ROTH P M, BISCHOF H. Hough-based tracking of non-rigid objects[J]. Computer Vision and Image Understanding, 2013, 117(10): 1245-1256.
DUFFNER S, GARCIA C. Pixeltrack: a fast adaptive algorithm for tracking non-rigid objects[C]∥Proceedings of the IEEE International Conference on Computer Vision. Los Alamitos, CA, USA: IEEE Computer Society, 2013: 2480-2487.
LOWE D G. Distinctive image features from scale invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
BAY H, TUYTELAARS T, VAN G L. Speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
GRABNER H, MATAS J, VAN GOOL L, et al. Tracking the invisible: learning where the object might be[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2010: 1285-1292.
YI K M, JEONG H, HEO B. Initialization-insensitive visual tracking through voting with salient local features[C]∥Proceedings of the IEEE International Conference on Computer Vision. Los Alamitos, CA, USA: IEEE Computer Society, 2013: 2912-2919.
GUO Yanwen, CHEN Ye, TANG Feng, et al. Object tracking using learned feature manifolds[J]. Computer Vision and Image Understanding, 2014, 118: 128-139.
LIN Haifeng, MA Yufeng, SONG Tao. Research on object tracking algorithm based on SIFT[J]. Acta Automatica Sinica, 2010, 36(8): 1204-1208.
WANG Shu, LU Huchuan, YANG Fan, et al. Superpixel tracking[C]∥Proceedings of the International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2011: 1323-1330.
KALAL Z, MIKOLAJCZYK K, MATAS J. Forward-backward error: automatic detection of tracking failures[C]∥Proceedings of the International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2010: 2756-2759.
WU Yi, LIM J, YANG M H. Online object tracking: a benchmark[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2013: 2411-2418.