Abstract
Human being has an extraordinary capability for motion perception due to its remarkable visual sensing system that makes it possible to perceive, distinguish and characterize the different moving elements of the environment. Thus, it extracts information through sensory experience and conducts reliable judgments based on intrinsic motion features, namely, location, direction, trajectory, magnitude, colors, boundary and shape.
Unfortunately, the same cannot be said for mobile robots. The critical nature of visual perception for these kinds of systems turns motion detection and analysis as one of the most relevant areas discussed on the literature, existing several models and methods to perform motion analysis in a variety of environments. This paper discusses motion analysis for mobile robots. A brief description about the complexity of motion perception based on moving observations and for surveillance applications is presented. In addition, the most often encountered approaches and future orientations are also discussed.
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
Preview
Unable to display preview. Download preview PDF.
References
Alexiadis, D.S., Sergiadis, G.D.: Motion estimation, segmentation and separation, using hypercomplex phase correlation, clustering techniques and graph-based optimization. Computer Vision and Image Understanding 113(2), 212–234 (2009)
Campbell, S., Naeem, W., Irwin, G.W.: A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Annual Reviews in Control 36(2), 267–283 (2012)
Cheng, J., Yang, J., Zhou, Y., Cui, Y.: Flexible background mixture models for foreground segmentation. Image and Vision Computing 24(5), 473–482 (2006)
Choi, J.H., Lee, D., Bang, H.: Tracking an unknown moving target from UAV: Extracting and localizing an moving target with vision sensor based on optical ow. In: 5th International Conference on Automation, Robotics and Applications (ICARA), pp. 384–389. IEEE (December 2011)
Correia, M.F.P.V.: Técnicas computacionais na percepc ao visual do movimento. PhD in electrical and computer engineering, Faculty of Engineering of the University of Porto, Porto, Portugal (2001)
Cucchiara, R., Prati, A., Vezzani, R.: Real-time motion segmentation from moving cameras. Real-Time Imaging 10(3), 127–143 (2004)
Di Paola, D., Milella, A., Cicirelli, G., Distante, A.: An autonomous mobile robotic system for surveillance of indoor environments. International Journal of Advanced Robotic Systems 7 (2009)
Ibrahim, A.W.N., Ching, P.W., Gerald Seet, G.L., Michael Lau, W.S., Czajewski, W.: Moving Objects Detection and Tracking Framework for UAV-based Surveillance. In: 2010 Fourth Pacific-Rim Symposium on Image and Video Technology, pp. 456–461. IEEE (November 2010)
Jung, B., Sukhatme, G.S.: Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In: 8th Conference on Intelligent Autonomous Systems, pp. 980–987 (March 2004)
Kim, I.S., Choi, H.S., Yi, K.M., Choi, J.Y., Kong, S.G.: Intelligent visual surveillance: A survey. International Journal of Control, Automation and Systems 8(5), 926–939 (2010)
Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Real-time foreground background segmentation using codebook model. Real-Time Imaging 11(3), 172–185 (2005)
Kundu, A., Jawahar, C.V., Madhava Krishna, K.: Realtime moving object detection from a freely moving monocular camera. In: IEEE International Confer- ence on Robotics and Biomimetics (ROBIO), pp. 1635–1640. IEEE (December 2010)
Li, J., Ai, H., Cui, J.: Moving Vehicle Detection in Dynamic Background from Airborne Monocular Camera. Energy Procedia 13, 3955–3961 (2011)
Liu, Y., Yao, H., Gao, W., Chen, X., Zhao, D.: Nonparametric background generation. Journal of Visual Communication and Image Representation 18(3), 253–263 (2007)
Longuet-Higgins, H.C., Prazdny, K.: The interpretation of a moving retinal image. Proceedings of the Royal Society of London 208(1173), 385–397 (1980)
Pinto, A.M., Paulo Moreira, A., Correia, M.V., Costa, P.G.: A ow-based motion perception technique for an autonomous robot system. Journal of Intelligent and Robotic Systems, 1–18 (2013), doi:10.1007/s10846-013-9999-z
Spagnolo, P., Orazio, T., Leo, M.M., Distante, A.: Moving object segmentation by background subtraction and temporal analysis. Image and Vision Computing 24(5), 411–423 (2006)
Su, S.-T., Chen, Y.-Y.: Moving Object Segmentation Using Improved Running Gaussian Average Background Model. In: Computing: Techniques and Applications (DICTA), pp. 24–31. IEEE (December 2008)
Tagliasacchi, M.: A genetic algorithm for optical ow estimation. Image and Vision Computing 25(2), 141–147 (2007)
Thakoor, N., Gao, J., Chen, H.: Automatic object detection in video sequences with camera in motion. In: Advanced Concepts for Intelligent Vision Systems, pp. 7–14 (August 2004)
Wang, Y., Tan, T., Loe, K.-F., Wu, J.-K.: A probabilistic approach for foreground and shadow segmentation in monocular image sequences. Pattern Recognition 38(11), 1937–1946 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pinto, A.M., Costa, P.G., Moreira, A.P. (2015). Introduction to Visual Motion Analysis for Mobile Robots. In: Moreira, A., Matos, A., Veiga, G. (eds) CONTROLO’2014 – Proceedings of the 11th Portuguese Conference on Automatic Control. Lecture Notes in Electrical Engineering, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-10380-8_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-10380-8_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10379-2
Online ISBN: 978-3-319-10380-8
eBook Packages: EngineeringEngineering (R0)