Object's Interaction Management by Means of a Fuzzy System within a Context-Based Tracking System

Tracking objects through interactions is a complex task, especially when it is important to be able to obtain the final trajectory followed by the object being track. This work proposes the use of a Context Layer to solve the problem of tracking through objects interactions, using a Fuzzy Reasoning System. Other authors have already used context information within a tracking system in order to improve its performance. The novelty of this work relies in that a Context Layer is created to reason over a general tracking system and thus improve the performance, instead of creating a context-based tracking algorithm. The experimentation shows how this Context Layer reasons over and improves a general tracking system.

[1]  Chris Stauffer,et al.  Estimating Tracking Sources and Sinks , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  Monique Thonnat,et al.  Tracking Groups of People for Video Surveillance , 2002 .

[3]  Pong C. Yuen,et al.  Automatic Acquisition of Context Models and its Application to Video Surveillance , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[4]  R. Nelson,et al.  Low level recognition of human motion (or how to get your man without finding his body parts) , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[5]  José R. Álvarez,et al.  Nature Inspired Problem-Solving Methods in Knowledge Engineering, Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007, Proceedings, Part II , 2007, IWINAC.

[6]  Trevor Darrell,et al.  Contextual recognition of head gestures , 2005, ICMI '05.

[7]  Weifeng Tian,et al.  Joint tracking algorithm using particle filter and mean shift with target model updating , 2006 .

[8]  Ramakant Nevatia,et al.  Tracking multiple humans in complex situations , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Tim J. Ellis,et al.  Augmented tracking with incomplete observation and probabilistic reasoning , 2006, Image Vis. Comput..

[10]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Miguel A. Patricio,et al.  Solving video-association problem with explicit evaluation of hypothesis using EDAs , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[12]  Ana M. Sánchez,et al.  Video tracking improvement using context-based information , 2007, 2007 10th International Conference on Information Fusion.

[13]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[14]  Miguel A. Patricio,et al.  Computational Intelligence in Visual Sensor Networks: Improving Video Processing Systems , 2008 .

[15]  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).

[16]  Ajith Abraham,et al.  Computational Intelligence in Multimedia Processing: Recent Advances , 2008 .

[17]  Miguel A. Patricio,et al.  Video Tracking Association Problem Using Estimation of Distribution Algorithms in Complex Scenes , 2007, IWINAC.