Occlusion Detection and Handling: A Review

Object tracking and detection is a classical research area in the field of computer vision from decades. Numerous kinds of applications are dependent on the area of object detection, such as advance driving assistance system, traffic surveillance, scene understanding, autonomous navigation etc. Many challenges still exist while detecting an object such as illusion, low visibility, cast shadows and most importantly occlusions of object. Occlusions occur under two categories, firstly its, self‐occlusion which means that, from a certain viewpoint, one part of an object is occluded by another part. Secondly, its inter-object occlusion which means when two objects being tracked occlude each other. We will review various occlusion handling methods that involved single and multiple cameras according to their application. In short, the objective of this paper is to deliberate in detail the problem of occlusion in object tracking and provide a concise review for the problem of occlusion handling under different categories and identify new trends.

[1]  Stephan Hasler,et al.  Using the Analytic Feature Framework for the Detection of Occluded Objects , 2013, ICANN.

[2]  Tieniu Tan,et al.  Tracking people through occlusions , 2004, ICPR 2004.

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

[4]  Chenyuan Zhang,et al.  A KLT-based approach for occlusion handling in human tracking , 2012, 2012 Picture Coding Symposium.

[5]  Yuichi Ohta,et al.  Stereo by integration of two algorithms with/without occlusion handling , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  Jeff McGough,et al.  Handling occlusion with an inexpensive array of cameras , 2014, 2014 Southwest Symposium on Image Analysis and Interpretation.

[7]  Xiaokang Yang,et al.  Multilevel Framework to Detect and Handle Occlusion , 2008 .

[8]  Dariu Gavrila,et al.  Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Van-Dung Hoang,et al.  Localization and tracking of same color vehicle under occlusion problem , 2012, 2012 9th France-Japan & 7th Europe-Asia Congress on Mechatronics (MECATRONICS) / 13th Int'l Workshop on Research and Education in Mechatronics (REM).

[10]  Jian Sun,et al.  Symmetric stereo matching for occlusion handling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Yo-Sung Ho,et al.  Efficient depth map generation with occlusion handling for various camera arrays , 2014, Signal Image Video Process..

[12]  Narendra Ahuja,et al.  Two-view Matching , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[13]  Stan Sclaroff,et al.  Improved Tracking of Multiple Humans with Trajectory Predcition and Occlusion Modeling , 1998 .

[14]  Yuqian Wu,et al.  Multi-person tracking-by-detection with local particle filtering and global occlusion handling , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[15]  Konrad Schindler,et al.  Explicit Occlusion Modeling for 3D Object Class Representations , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Geoffrey Egnal,et al.  Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Stephan Hasler,et al.  A Two-Stage Classifier Architecture for Detecting Objects under Real-World Occlusion Patterns , 2014, ICANN.

[18]  Richard Szeliski,et al.  Handling occlusions in dense multi-view stereo , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Rashid Mehmood,et al.  Occlusion handling in meanshift tracking using adaptive window Normalized Cross Correlation , 2014, Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th - 18th January, 2014.

[20]  Konrad Schindler,et al.  Improved Multi-Person Tracking with Active Occlusion Handling , 2009, ICRA 2009.

[21]  Bing-Fei Wu,et al.  A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking , 2014, IEEE Transactions on Industrial Electronics.

[22]  James J. Little,et al.  Direct evidence for occlusion in stereo and motion , 1990, Image Vis. Comput..

[23]  Seiichi Mita,et al.  Occlusion handling using discriminative model of trained part templates and conditional random field , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[24]  Qinping Zhao,et al.  Occlusion cues for image scene layering , 2013, Comput. Vis. Image Underst..

[25]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..

[26]  Quan Pan,et al.  Real-time multiple objects tracking with occlusion handling in dynamic scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Paulo G. Costa,et al.  An architecture for visual motion perception of a surveillance-based autonomous robot , 2014, 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[28]  Gerd Hirzinger,et al.  Real-time visual tracking of 3D objects with dynamic handling of occlusion , 1997, Proceedings of International Conference on Robotics and Automation.

[29]  Yo-Sung Ho,et al.  Efficient disparity map estimation using occlusion handling for various 3D multimedia applications , 2011, IEEE Transactions on Consumer Electronics.

[30]  Alan L. Yuille,et al.  Occlusions and binocular stereo , 1992, International Journal of Computer Vision.

[31]  Shaogang Gong,et al.  Tracking Multiple People Under Occlusion Using Multiple Cameras , 2000, BMVC.

[32]  Kwanghoon Sohn,et al.  Cost Aggregation and Occlusion Handling With WLS in Stereo Matching , 2008, IEEE Transactions on Image Processing.

[33]  Bernt Schiele,et al.  Monocular 3D scene understanding with explicit occlusion reasoning , 2011, CVPR 2011.

[34]  Ramón Moreno,et al.  A machine learning based intelligent vision system for autonomous object detection and recognition , 2013, Applied Intelligence.

[35]  Jean-Yves Hervé,et al.  Visual tracking of hand posture with occlusion handling , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[36]  Konrad Schindler,et al.  Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Bo Hu,et al.  Robust Occlusion Handling in Object Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  N. H. C. Yung,et al.  A novel method for resolving vehicle occlusion in a monocular traffic-image sequence , 2004, IEEE Transactions on Intelligent Transportation Systems.