Moving object tracking based on mobile robot vision

The paper describes a robotic application that tracks a moving object by utilizing a mobile robot with capacity of avoiding obstacles. Real-time tracking algorithm is based on mobile robot vision using adaptive color matching and Kalman filter. Adaptive color matching is used to obtain motion vectors of moving object in the robot coordinate system. It can adjust color matching threshold adaptively to reduce the influence of lighting variations in the scene. Kalman filter is applied to design a view window. View window, which contains the next position of the moving object estimated by Kalman filter, is determined on image plane to reduce the image processing area. Color matching threshold can adjust itself adaptively in view window. A virtual targets based algorithm is also presented for real-time obstacle avoidance in this paper. Experimental results show that the tracking algorithm can adapt to lighting variations and has good tracking precision. It can also avoid obstacles smoothly while tracking moving object. Furthermore, it can be implemented in real time.

[1]  Bin Lei,et al.  A Fuzzy Behaviours Fusion Algorithm for Mobile Robot Real-time Path Planning in Unknown Environment , 2007, 2007 IEEE International Conference on Integration Technology.

[2]  João Manuel R. S. Tavares.  Tracking moving objects in image sequences , 2010 .

[3]  Yoram Koren,et al.  Real-time obstacle avoidance for fast mobile robots in cluttered environments , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[4]  Youngseok Cho,et al.  High speed tracking algorithm of moving object , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[5]  Éric Marchand,et al.  Experiments with robust estimation techniques in real-time robot vision , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Hyung-Il Choi,et al.  Moving object tracking using active models , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[7]  K. Takaya,et al.  Tracking moving objects in a video sequence by the neural network trained for motion vectors , 2005, PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005..

[8]  Jean-Charles Noyer,et al.  Object detection and tracking using the particle filtering , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[9]  Jiawei Li,et al.  Obstacle avoidance using fuzzy neural networks , 1998, Proceedings of 1998 International Symposium on Underwater Technology.

[10]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[11]  Yangmin Li,et al.  Smooth Formation Navigation of Multiple Mobile Robots for Avoiding Moving Obstacles , 2006 .

[12]  Panos E. Trahanias,et al.  Predictive control of robot velocity to avoid obstacles in dynamic environments , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[13]  Ioannis Pitas,et al.  Prediction and tracking of moving objects in image sequences , 2000, IEEE Trans. Image Process..

[14]  Chung-Hao Chen,et al.  A Moving Object Tracked by A Mobile Robot with Real-Time Obstacles Avoidance Capacity , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  Brett Browning,et al.  Real-time, adaptive color-based robot vision , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  S. Chen,et al.  Using shape feature matching to track moving objects in image sequences , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[17]  Boumediene Belkhouche,et al.  On the tracking and interception of a moving object by a wheeled mobile robot , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..