Abstract
Assuming a moving camera, detection of moving objects is a challenging task. This is mainly due to the difficulties to distinguish between objects motion and background motion, introduced by the camera. The proposed real time system, based on previous work without camera movement, is able to discriminate well the two kind of motions, thanks to a robust global motion vector removal, which preserves objects identified in the previous steps. The system reaches high performances just using input Optical Flow, without any assumptions about environmental conditions and camera motion.
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
Yazdy, M., Bouwmans, T.: New trends on moving object detection in video images captured by a moving camera: a survey. Comput. Sci. Rev. 28, 157–177 (2018)
Panda, D.K., Meher, S.: Detection of moving objects using fuzzy color difference histogram based background subtraction. IEEE Signal Process. Lett. 23, 45–49 (2016)
Anandhalli, M., Baligar, V.P.: Improvised approach using background subtraction for vehicle detection. In: IEEE International Advance Computing Conference (IACC) (2015)
Harrouss, O.E., Moujahid, D., Tairi, H.: Motion detection based on the combining of the background subtraction and spatial color information. In: Intelligent Systems and Computer Vision (ISCV) 2015
Huynh-The, T., Banos, O., Lee, S., Kang, B.H., Kim, E., Le-Tien, T.: NIC: a robust background extraction algorithm for foreground detection in dynamic scenes. IEEE Trans. Circ. Syst. Video Technol. 27, 1478–1490 (2016)
Huang, C., Hung, M.: Target motion compensation with optical flow clustering during visual tracking. In: IEEE 11th International Conference on Networking, Sensing and Control (ICNSC) (2014)
Wong, C., Siu, W.C., Barnes, S., Jennings, P., Fong, B.: Shared-use motion vector algorithm for moving objects detection for automobiles. In: IEEE International Conference on Consumer Electronics (ICCE) (2016)
Hariyono, J., Jo, K.: Detection of pedestrian crossing road. In: IEEE International Conference on Image Processing (ICIP) (2015)
Liang, C., Juang, C.: Moving object classification using a combination of static appearance features and spatial and temporal entropy values of optical flows. IEEE Trans. Intell. Transp. Syst. 16, 3453–3464 (2015)
Hsu-Yung, C., Chih-Chia, W., Yi-Ying, C.: Vehicle detection in aerial surveillance using dynamic Bayesian networks. IEEE Trans. Image Process. 21, 2152–2159 (2012)
Chen, L., Jiang, Z., Yang, J., Ma, Y.: A coarse-to-fine approach for vehicles detection from aerial images. In: International Conference on Computer Vision in Remote Sensing (CVRS) (2012)
Cheraghi, S.A., Sheikh, U.U.: Moving object detection using image registration for a moving camera platform. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (2012)
Gleason, J., Nefian, A.V., Bouyssounousse, X., Fong, T., Bebis, G.: Vehicle detection from aerial imagery. In: IEEE International Conference on Robotics and Automation (2011)
Zheng, Z., Wang, X., Zhou, G., Jiang, L.: Vehicle detection based on morphology from highway aerial images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2012)
Kembhavi, A., Harwood, D., Davis, L.S.: Vehicle detection using partial least squares. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1250–1265 (2011)
Sadeghi-Tehran, P., Angelov, P.: ATDT: autonomous template-based detection and tracking of objects from airborne camera. In: Filev, D., et al. (eds.) Intelligent Systems’2014. AISC, vol. 323, pp. 555–565. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11310-4_48
Kim, S.W., Yun, K., Yi, K.M., Kim, S.J., Choi, J.Y.: Detection of moving objects with a moving camera using non-panoramic background model. Mach. Vis. Appl. 25, 1015–1028 (2012)
Yi, K.M., Yun, K., Kim, S.W., Chang, H.J., Jeong, H., Choi, J.Y.: Detection of moving objects with non-stationary cameras in 5.8 ms: bringing motion detection to your mobile device. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2013)
Kim, J., Wang, X., Wang, H., Zhu, C., Kim, D.: Fast moving object detection with non-stationary background. Multimedia Tools Appl. 67(1), 311–335 (2013)
Sadeghi-Tehran, P., Clarke, C., Angelov, P.: A real-time approach for autonomous detection and tracking of moving objects from UAV. In: IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (2014)
Kim, J., Ye, G., Kim, D.: Moving object detection under free-moving camera. In: Proceedings of IEEE International Conference on Image Processing (ICIP) (2010)
Spampinato, G., Bruna, A., Curti, S., D’Alto, V.: Advanced low cost clustering system. In: 6th International Conference on Image Processing Theory, Tools and Applications (IPTA) (2016)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(11), 381–395 (1981)
Spampinato, G., Bruna, A., Farinella, G.M., Battiato, S., Puglisi, G.: Fast and Low Power Consumption Outliers Removal for Motion Vector Estimation. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 70–80. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25903-1_7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Spampinato, G., Bruna, A., Curti, S., Giacalone, D. (2019). Advanced Moving Camera Object Detection. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
