Keyframe-based online object learning and detection

In this paper, we propose a keyframe-based online object learning and detection method. To manage appearance changes of target objects, the proposed method incrementally updates an object database using detection results. One of the major problems in updating the appearance model is that the object model can gradually be degraded by accumulated errors and biased to specific views. To solve this problem, our object model is updated according to the selected keyframes, which not only help memorize important views of target objects, but also prevent the holistic appearance model from overfitting. The database is represented as a graph of the registered images, and the importance of the database images is measured by analyzing the constructed graph. Then, the redundant or less important images are discarded from the database. As a result, the database is efficiently maintained while new views of the objects are gradually added. The experimental results demonstrate that the proposed algorithm efficiently maintains the object database and improves the detection performance compared to previous incremental object learning and detection algorithms.

[1]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Ralph R. Martin,et al.  Incremental Eigenanalysis for Classification , 1998, BMVC.

[3]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[4]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

[8]  Il Hong Suh,et al.  Incremental learning from a single seed image for object detection , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Matthew Brand,et al.  Incremental Singular Value Decomposition of Uncertain Data with Missing Values , 2002, ECCV.

[10]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[11]  Ming-Hsuan Yang,et al.  Incremental Learning for Visual Tracking , 2004, NIPS.

[12]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[15]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Changsheng Xu,et al.  Structural Sparse Tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[21]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.