nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones

verfasst von : Hazal Lezki, I. Ahu Ozturk, M. Akif Akpinar, M. Kerim Yucel, K. Berker Logoglu, Aykut Erdem, Erkut Erdem

Erschienen in: Computer Vision – ECCV 2018 Workshops

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Moving object detection is an imperative task in computer vision, where it is primarily used for surveillance applications. With the increasing availability of low-altitude aerial vehicles, new challenges for moving object detection have surfaced, both for academia and industry. In this paper, we propose a new approach that can detect moving objects efficiently and handle parallax cases. By introducing sparse flow based parallax handling and downscale processing, we push the boundaries of real-time performance with 16 FPS on limited embedded resources (a five-fold improvement over existing baselines), while managing to perform comparably or even improve the state-of-the-art in two different datasets. We also present a roadmap for extending our approach to exploit multi-modal data in order to mitigate the need for parameter tuning.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery

Nächstes Kapitel UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

http://www.boeing.com/defense/phantom-eye/.

https://aerixdrones.com/products/vidius-the-worlds-smallest-fpv-drone.

https://www.nvidia.com/en-us/autonomous-machines/embedded-systems-dev-kits-modules/.

Authors of [17] have reported their results with this metric, therefore we give these results to compare our work.

Clarke, R.: Understanding the drone epidemic. Comput. Law Secur. Rev. 30(3), 230–246 (2014)CrossRef

Zhong, J., Lei, T., Yao, G.: Robust vehicle detection in aerial images based on cascaded convolutional neural networks. Sensors 17(12), 2720 (2017)CrossRef

Li, F., Li, S., Zhu, C., Lan, X., Chang, H.: Cost-effective class-imbalance aware cnn for vehicle localization and categorization in high resolution aerial images. Remote Sens. 9(5), 494 (2017)CrossRef

Tijtgat, N., Van Ranst, W., Volckaert, B., Goedemé, T., De Turck, F.: Embedded real-time object detection for a UAV warning system. In: The International Conference on Computer Vision, ICCV 2017, pp. 2110–2118 (2017)

Sommer, L.W., Schuchert, T., Beyerer, J.: Fast deep vehicle detection in aerial images. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 311–319. IEEE (2017)

Stek, T.D.: Drones over Mediterranean landscapes. The potential of small UAV’s (drones) for site detection and heritage management in archaeological survey projects: a case study from Le Pianelle in the Tappino Valley, Molise (Italy). J. Cultural Herit. 22, 1066–1071 (2016)CrossRef

Barekatain, M., et al.: Okutama-action: an aerial view video dataset for concurrent human action detection. In: 1st Joint BMTT-PETS Workshop on Tracking and Surveillance, CVPR, pp. 1–8 (2017)

Pestana, J., Sanchez-Lopez, J.L., Campoy, P., Saripalli, S.: Vision based GPS-denied object tracking and following for unmanned aerial vehicles. In: 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6. IEEE (2013)

Dang, C.T., Pham, T.B., Truong, N.V., et al.: Vision based ground object tracking using AR drone quadrotor. In: 2013 International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 146–151. IEEE (2013)

10.

Chen, P., Dang, Y., Liang, R., Zhu, W., He, X.: Real-time object tracking on a drone with multi-inertial sensing data. IEEE Trans. Intell. Transp. Syst. 19(1), 131–139 (2018)CrossRef

11.

Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: The IEEE International Conference on Computer Vision (ICCV), vol. 1 (2017)

12.

Kanistras, K., Martins, G., Rutherford, M.J., Valavanis, K.P.: A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 221–234. IEEE (2013)

13.

Du, D., et al.: The unmanned aerial vehicle benchmark: object detection and tracking. arXiv preprint arXiv:1804.00518 (2018)

14.

Wang, S., et al.: TorontoCity: seeing the world with a million eyes. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3028–3036. IEEE (2017)

15.

Xia, G.S., et al.: DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of CVPR (2018)

16.

Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27CrossRef

17.

Berker Logoglu, K., et al.: Feature-based efficient moving object detection for low-altitude aerial platforms. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2017

18.

Lam, D., et al.: xView: objects in context in overhead imagery. arXiv preprint arXiv:1802.07856 (2018)

19.

Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q.: Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437 (2018)

20.

Yu, Q., Medioni, G.: A GPU-based implementation of motion detection from a moving platform (2008)

21.

Kryjak, T., Komorkiewicz, M., Gorgon, M.: Real-time moving object detection for video surveillance system in FPGA. In: 2011 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 1–8. IEEE (2011)

22.

Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151–1163 (2002)CrossRef

23.

Eveland, C., Konolige, K., Bolles, R.C.: Background modeling for segmentation of video-rate stereo sequences. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 266–271. IEEE (1998)

24.

Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)CrossRef

25.

Suganuma, N., Kubo, T.: Fast dynamic object extraction using stereovision based on occupancy grid maps and optical flow. In: 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 978–983. IEEE (2011)

26.

Rodríguez-Canosa, G.R., Thomas, S., Del Cerro, J., Barrientos, A., MacDonald, B.: A real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. Remote Sens. 4(4), 1090–1111 (2012)CrossRef

27.

Kimura, M., Shibasaki, R., Shao, X., Nagai, M.: Automatic extraction of moving objects from UAV-borne monocular images using multi-view geometric constraints. In: International Micro Air Vehicle Conference and Competition, IMAV 2014, Delft, The Netherlands, 12–15 August 2014, Delft University of Technology (2014)

28.

Salgian, G., Bergen, J., Samarasekera, S., Kumar, R.: Moving target indication from a moving camera in the presence of strong parallax. Technical report, DTIC Document (2006)

29.

Dey, S., Reilly, V., Saleemi, I., Shah, M.: Detection of independently moving objects in non-planar scenes via multi-frame monocular epipolar constraint. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 860–873. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_62CrossRef

30.

Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 266–280 (2000)CrossRef

31.

Joshi, K.A., Thakore, D.G.: A survey on moving object detection and tracking in video surveillance system. Int. J. Soft Comput. Eng. 2(3), 44–48 (2012)

32.

Cao, X., Lan, J., Yan, P., Li, X.: Vehicle detection and tracking in airborne videos by multi-motion layer analysis. Mach. Vis. Appl. 23(5), 921–935 (2012)CrossRef

33.

Irani, M., Anandan, P.: A unified approach to moving object detection in 2D and 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 20(6), 577–589 (1998)CrossRef

34.

Kang, J., Cohen, I., Medioni, G., Yuan, C.: Detection and tracking of moving objects from a moving platform in presence of strong parallax. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 10–17. IEEE (2005)

35.

Yuan, C., Medioni, G., Kang, J., Cohen, I.: Detecting motion regions in the presence of a strong parallax from a moving camera by multiview geometric constraints. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1627–1641 (2007)CrossRef

36.

Kundu, A., Krishna, K.M., Sivaswamy, J.: Moving object detection by multi-view geometric techniques from a single camera mounted robot. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4306–4312, October 2009

37.

Minaeian, S., Liu, J., Son, Y.J.: Effective and efficient detection of moving targets from a UAV’s camera. IEEE Trans. Intell. Transp. Syst. 19, 497–506 (2018)CrossRef

38.

Castelli, T., Trémeau, A., Konik, H., Dinet, E.: Moving object detection for unconstrained low-altitude aerial videos, a pose-independant detector based on artificial flow. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 42–47. IEEE (2015)

39.

Wu, Y., He, X., Nguyen, T.Q.: Moving object detection with a freely moving camera via background motion subtraction. IEEE Trans. Circuits Syst. Video Technol. 27(2), 236–248 (2017)CrossRef

40.

Makino, K., Shibata, T., Yachida, S., Ogawa, T., Takahashi, K.: Moving-object detection method for moving cameras by merging background subtraction and optical flow methods. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 383–387, November 2017

41.

Ali, S., Shah, M.: COCOA: tracking in aerial imagery. In: Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications III, vol. 6209, p. 62090D. International Society for Optics and Photonics (2006)

42.

Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32CrossRef

43.

Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision (1981)

44.

Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)MATH

45.

Collins, R., Zhou, X., Teh, S.K.: An open source tracking testbed and evaluation web site. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2005), vol. 2, p. 35 (2005)

46.

Hasan, M.: Integrating geometric, motion and appearance constraints for robust tracking in aerial videos (2013)

47.

Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 983–990. IEEE (2009)

48.

Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: British Machine Vision Conference, vol. 1, p. 6 (2006)

Titel: Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones
verfasst von: Hazal Lezki
I. Ahu Ozturk
M. Akif Akpinar
M. Kerim Yucel
K. Berker Logoglu
Aykut Erdem
Erkut Erdem
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018 Workshops
Print ISBN: 978-3-030-11011-6

Electronic ISBN: 978-3-030-11012-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-11012-3_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"