Object Detection and Tracking -- A Survey

Object tracking is one of the major fundamental challenging problems in computer vision applications due to difficulties in tracking of objects can arises due to intrinsic and extrinsic factors like deformation, camera motion, motion blur and occlusion. This paper proposes a literature review on several state -- of -- the-art object detection and tracking algorithms in order to reduce the tracking drift.

[1]  Huchuan Lu,et al.  Incremental MPCA for Color Object Tracking , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Thang Ba.  Co-training Framework of Generative and Discriminative Trackers with Partial Occlusion Handling , 2010 .

[4]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, CVPR.

[5]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[6]  Huchuan Lu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Online Object Tracking with Sparse Prototypes , 2022 .

[7]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[8]  Qi Zhao,et al.  Co-Tracking Using Semi-Supervised Support Vector Machines , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[10]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Xiaoqin Zhang,et al.  Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking , 2011, International Journal of Computer Vision.