Abstract
To detect moving objects is a difficult task for various image processing and computer vision applications viz., motion segmentation, object classification and identification, behavior understanding, event detection, object tracking and object locating. The process becomes even more difficult with moving camera as compared to static camera as both camera and object motions are combined in the detection process. Moreover, almost all real time video sequences incorporate pan/tilt/zoom camera which makes it essential to detect objects in moving cameras. This paper presents a survey of various moving object detection techniques in moving cameras and gives insight picture of the methods like background subtraction, optical flow, feature based object detection and blob analysis. It also mentions pros and cons of every technique used so far individually. The use of these approaches and progress made over years has been tracked and elaborated.
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
Dongxiang Zhou, Liangfen Wang, XuanpingCai, and Yunhui Liu.: Detection of Moving Targets with a Moving camera. In: the Proceedings of the IEEE International Conference on Robotics and Biometric, pp. 677–681, Guilin, China (2009).
Fischler, Martin A., and Robert C. Bolles.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Communications of the ACM, pp. 381–395 (1981).
J. Shi, C. Tomasi.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994).
Zou, Xiaochun, Xinbo Zhao, and Zheru Chi.: A robust background subtraction approach with a moving camera. In: 7th International Conference on Computing and Convergence Technology (ICCCT), IEEE (2012).
Carlo Tomasi and Takeo Kanade.: Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ. Pittsburgh, CMU-CS-91–-132 (1991).
Zamalieva, Daniya, Alper Yilmaz, and James W. Davis.: Exploiting temporal geometry for moving camera background subtraction. In: 22nd International Conference on Pattern Recognition (ICPR). IEEE (2014).
Sinha, Sudipta N., et al.: GPU-based video feature tracking and matching., Workshop on Edge Computing Using New Commodity Architectures. Vol. 27 (2006).
Hu, Wu-Chih, et al.: Moving object detection and tracking from video captured by moving camera. In: Journal of Visual Communication and Image Representation pp 164–180, vol 30 (2015).
Zhang, Guofeng, et al.: Moving object extraction with a hand-held camera. In: 11th International Conference on Computer Vision, (ICCV), IEEE (2007).
G. Zhang, X. Qin, W. Hua, T.-T. Wong, P.-A. Heng, and H. Bao.: Robust metric reconstruction from challenging video sequences. In: CVPR (2007).
Cheng-Ming huang, Yi-Ru Chen and Li-Chen Fu.: Real time object detection and tracking on a moving camera Platform. In: IEEE International Joint Conference (ICROS-SICE), pp. 717–722, Fukuoka (2009).
Rahul Kumar Namdev, AbhijitKundu, K Mahadev Krishna and C.V. Jawahar.: Motion segmentation of multiple objects from a freely moving monocular camera. In: IEEE International Conference on Robotics and Automation, pp. 4092–4099, River Centre, Saint Paul, Minnesota, USA (2012).
Yamaguchi, Koichiro, Takeo Kato, and Yoshiki Ninomiya.: Vehicle ego-motion estimation and moving object detection using a monocular camera. In: 18th International Conference on Pattern Recognition, (ICPR) Vol. 4. IEEE (2006).
Yuan, Chang, et al.: Detecting motion regions in the presence of a strong parallax from a moving camera by multiview geometric constraints. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1627–1641 (2007).
Minematsu, Tsubasa, et al.: Evaluation of foreground detection methodology for a moving camera. In: 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), IEEE (2015).
Hyukseong Kwon, Youngrock Yoon and Jae Byung Park.: Person Tracking with a Mobile Robot using Two Uncalibrated Independently Moving Cameras. In: IEEE Proceedings of International Conference on Robotics and Automation, pp. 2877–2883, Spain (2005).
Teutsch, Michael, Wolfgang Kruger, and Jurgen Beyerer.: Evaluation of object segmentation to improve moving vehicle detection in aerial videos. In: 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE (2014).
C. Stauffer.: Learning patterns of activity using real-time tracking. IEEE transactions on pattern analysis and machine intelligence, Vol 22 (2000).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Neeraj, Akashdeep (2017). A Comprehensive Analysis of Moving Object Detection Approaches in Moving Camera. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_30
Download citation
DOI: https://doi.org/10.1007/978-981-10-3818-1_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3817-4
Online ISBN: 978-981-10-3818-1
eBook Packages: EngineeringEngineering (R0)