Abstract
Moving objects detecting and tracking is important for future Unmanned Aerial Vehicles (UAVs). We propose a new approach to detect and track moving objects from the flying UAV. First, estimate the global-motion of the background by tracking features selected by KLT algorithm from frame to frame. In order to avoid features located on the foreground objects participating in motion estimation, feature effectiveness evaluation is employed. Then compensate the background with the transform model computed by RANSAC. Define the undefined area before applying frame difference method to the compensated frame and the current frame. Then initialize the tracking module with information obtained from the detecting module, which overcomes shortcomings of artificial orientation of traditional tracking algorithms. For tracking fast and robustly from UAVs, we design a new tracking algorithm by fusing Kalman prediction and Mean Shift Search together. The experimental results presented effectiveness of the whole detecting and tracking approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang D (1998) Unsupervised video segmentation based on watersheds and temporal tracking. Circuits Syst Video Technol IEEE Trans 8(5):539–546
Wong K, Spetsakis M (2002) Motion segmentation and tracking. In: International conference on vision interface, Citeseer, pp 80–87
Cavallaro A, Ebrahimi T (2000) Video object extraction based on adaptive background and statistical change detection. Proc of SPIE 4310:465
Shi J, Tomasi C (1994) Good features to track. In: Proceedings of the CVPR’94. IEEE computer society conference on computer vision and pattern recognition, pp 593–600
Tomasi C, Kanade T (1991) Detection and tracking of point features. Carnegie Mellon Univ, School of Computer Science
Trucco E, Petillot Y, Ruiz I, Plakas K, Lane D (2000) Feature tracking in video and sonar subsea sequences with applications. Comput Vis Image Underst 79:92–122
Jurie F (1998) Tracking objects with a recognition algorithm. Pattern Recogn Lett 19(3):331–340
Drummond T, Cipolla R (2002) Real-time visual tracking of complex structures. Pattern Anal Mach Intell IEEE Trans 24(7):932–946
Jung B, Sukhatme G (2004) Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In: International conference on intelligent autonomous systems, Citeseer, pp 980–987
Srinivasan S, Chellappa R (1997) Image stabilization and mosaicking using the overlapped basis optical flow field. In: The proceedings of the IEEE international conference on image processing, vol 3, pp 356–359
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. Pattern Anal Mach Intell IEEE Trans 25(5):564–577
Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Xin S (2006) The matching method based on ransac algorithm for estimation of the fundamental matrix. J Shanghai Dianji Univ 9(004):66–69
Comaniciu D, Meer P (1999) Mean shift analysis and applications. In: The proceedings of the seventh IEEE international conference on computer vision, vol 2, pp 1197–1203
Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. Inf Theory IEEE Trans 21(1):32–40
Acknowledgments
We appreciate the contributions made by the open source communities to the humanity. We especially thank the developers and sponsors of OpenCV libraries for their valuable work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Su, B., Wang, H., Liang, X., Ji, H. (2014). Moving Objects Detecting and Tracking for Unmanned Aerial Vehicle. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-37835-5_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37834-8
Online ISBN: 978-3-642-37835-5
eBook Packages: EngineeringEngineering (R0)