Skip to main content
SpringerLink
Log in

Pedestrian Detection for UAVs Using Cascade Classifiers and Saliency Maps

  • Conference paper
  • First Online:
Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10306))

Included in the following conference series:

Abstract

In this paper, we proposed algorithm and dataset for pedestrian detection focused on applications with micro multi rotors UAV (Unmanned Aerial Vehicles). For training dataset we capture images from surveillance cameras at different angles and altitudes. We propose a method based on HAAR-LBP (Local Binary Patterns) cascade classifiers with Adaboost (Adaptive Boosting) training and, additionally we combine cascade classifiers with saliency maps for improving the performance of the pedestrian detector. We evaluate our dataset by the implementation of the HOG (Histogram of oriented gradients) algorithm with Adaboost training and, finally, algorithm performance is compared with other approaches from the state of art. The results shows that our dataset is better for pedestrian detection in UAVs, HAAR-LBP have better characteristics than HAAR like features and the use of saliency maps improves the performance of detectors due to the elimination of false positives in the image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Torresan, H.: Advanced surveillance systems: combining video and thermal imagery for pedestrian detection. In: Proceedings of SPIE, pp. 506–515 (2004)

    Google Scholar 

  2. Zhang, L.Z.L., Wu, B.W.B., Nevatia, R.: Pedestrian detection in infrared images based on local shape features. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  3. Aguilar, W.G., Angulo, C., Costa, R., Molina, L.: Control autónomo de cuadricopteros para seguimiento de trayectorias. In: Memorias del IX Congreso de Ciencia y Tecnología ESPE 2014 (2014)

    Google Scholar 

  4. Aguilar, W.G., Angulo, C.: Compensación de los Efectos Generados en la Imagen por el Control de Navegación del Robot Aibo ERS 7. In: Memorias del VII Congreso de Ciencia y Tecnolgia, ESPE 2012, pp. 165–170, June 2012

    Google Scholar 

  5. Jafari, O.H., Mitzel, D., Leibe, B.: Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 5636–5643, April 2016

    Google Scholar 

  6. Kobilarov, M., Sukhatme, G., Hyams, J., Batavia, P.: People tracking and following with mobile robot using an omnidirectional camera and a laser. In: 2006 IEEE International Conference on Robotics and Automation 2006 ICRA, pp. 557–562, May 2006

    Google Scholar 

  7. Aguilar, W.G., Casaliglla, V., Pólit, J.: Obstacle avoidance based-visual navigation for micro aerial vehicles. Electronics 6(1), 10 (2017)

    Article  Google Scholar 

  8. Cabras, P., Rosell, J., Pérez, A., Aguilar, W.G., Rosell, A.: Haptic-based navigation for the virtual bronchoscopy. In: 18th IFAC World Congress, Milano, Italy (2011)

    Google Scholar 

  9. Aguilar, W.G., Morales, S.: 3D environment mapping using the Kinect V2 and path planning based on RRT algorithms. Electronics 5(4), 70 (2016)

    Article  Google Scholar 

  10. Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Proceedings of the 6th European Conference on Computer Vision, vol. 1843, pp. 37–49 (2000)

    Google Scholar 

  11. Gerónimo, D., López, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)

    Article  Google Scholar 

  12. Aguilar, W.G., Angulo, C.: Real-time video stabilization without phantom movements for micro aerial vehicles. EURASIP J. Image Video Process. 1, 1–13 (2014)

    Google Scholar 

  13. Aguilar, W.G., Angulo, C.: Real-time model-based video stabilization for microaerial vehicles. Neural Process. Lett. 43(2), 459–477 (2016)

    Article  Google Scholar 

  14. Aguilar, W.G., Angulo, C.: Robust video stabilization based on motion intention for low-cost micro aerial vehicles. In: 2014 11th International Multi-Conference on Systems, Signals Devices (SSD), pp. 1–6 (2014)

    Google Scholar 

  15. Rudol, P., Doherty, P.: Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery

    Google Scholar 

  16. Aguilar, W.G., Luna, M.A., Moya, J.F., Abad, V., Parra, H., Ruiz, H.: Pedestrian detection for UAVs using cascade classifiers with meanshift. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 509–514 (2017)

    Google Scholar 

  17. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. 20, 1254–1259 (1998)

    Article  Google Scholar 

  18. Dalal, N., Triggs, W.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision Pattern Recognition CVPR 2005, vol. 1, no. 3, pp. 886–893 (2004)

    Google Scholar 

  19. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 304–311 (2009)

    Google Scholar 

  20. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)

    Google Scholar 

  21. Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Proceedings of Computer Vision-ECCV 2014 Workshop, pp. 613–627 (2014)

    Google Scholar 

  22. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)

    Article  Google Scholar 

  23. Papageorgiou, C., Poggio, T.: Trainable system for object detection. Int. J. Comput. Vis. 38(1), 15–33 (2000)

    Article  MATH  Google Scholar 

  24. Overett, G., Petersson, L., Brewer, N., Andersson, L., Pettersson, N.: A new pedestrian dataset for supervised learning. In: Proceedings of IEEE Intelligent Vechiles Symposium, pp. 373–378 (2008)

    Google Scholar 

  25. Ess, A., Leibe, B., Schindler, K., van Gool, L.: Robust multiperson tracking from a mobile platform. IEEE Trans. Pattern Anal. Mach. Intell. 31(10), 1831–1846 (2009)

    Article  Google Scholar 

  26. Wojek, C., Walk, S., Schiele, B.: Multi-Cue onboard pedestrian detection. In: 2009 IEEE Computer Society Confernce on Computer Vision Pattern Recognition Workshops CVPR Workshops 2009, pp. 794–801 (2009)

    Google Scholar 

  27. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2179–2195 (2009)

    Article  Google Scholar 

  28. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision Pattern Recognition, pp. 1–9 (2001)

    Google Scholar 

  29. Zhu, Q., Avidan, S., Yeh, M.C., Cheng, K.T.: Fast human detection using a cascade of histograms of oriented gradients. IEEE Conf. Comput. Vis. Pattern Recognit. 2, 1491–1498 (2006)

    Google Scholar 

  30. Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Pattern Recognition Symposium, pp. 82–91 (2008)

    Google Scholar 

  31. Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision ICCV, pp. 32–39 (2009)

    Google Scholar 

  32. Imamura, Y., Okamoto, S., Lee, J.H.: Human tracking by a multi-rotor drone using HOG features and linear SVM on images captured by a monocular camera. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 8–13 (2016)

    Google Scholar 

  33. Lioulemes, A., Galatas, G., Metsis, V., Mariottini, G.L., Makedon, F.: Safety challenges in using AR. drone to collaborate with humans in indoor environments. In: Proceedings of 7th International Conference on Pervasive Technologies Related to Assistive Environments, p. 33 (2014)

    Google Scholar 

  34. Blondel, P., Potelle, A., Pegard, C., Lozano, R.: Human detection in uncluttered environments: from ground to UAV view. In: 2014 13th International Conference on Control Automation Robotics and Vision ICARCV 2014, pp. 76–81 (1997)

    Google Scholar 

  35. Andriluka, M., Schnitzspan, P., Meyer, J., Kohlbrecher, S., Petersen, K., Von Stryk, O., Roth, S., Schiele, B.: Vision based victim detection from unmanned aerial vehicles. In: 2010 IEEE/RSJ International Conference on Intelligent Robot and System (IROS), pp. 1740–1747, October 2010

    Google Scholar 

  36. De Smedt, F., Hulens, D., Goedeme, T.: On-board real-time tracking of pedestrians on a UAV. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop, pp. 1–8, October 2015

    Google Scholar 

  37. Rudol, P., Doherty, P., Science, I.: Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery. In: 2008 IEEE Aerospace Conference, pp. 1–8 (2008)

    Google Scholar 

  38. Gąszczak, A., Breckon, T.P., Han, J.: Real-time people and vehicle detection from UAV imagery. In: IS&T/SPIE Electron. Imaging, pp. 8–11, January 2011

    Google Scholar 

  39. Siam, M., Elhelw, M.: Robust autonomous visual detection and tracking of moving targets in UAV imagery. In: Proceedings of International Conference on Signal Process (ICSP), vol. 2, pp. 1060–1066, December (2012)

    Google Scholar 

  40. Wang, L., He, D.: Texture classification using texture spectrum. Pattern Recognit. 23, 905–910 (1990)

    Article  Google Scholar 

  41. Papageorgiou, C.P. Oren, M.: A general framework for object detection. In: IEEE International Conference on Computer Vision, pp. 555–562, January 1998

    Google Scholar 

  42. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)

    Article  MATH  Google Scholar 

  43. Moosmann, F., Larlus, D., Jurie, F.: Learning saliency maps for object categorization. In: International Workshop on the Representation and Use of Prior Knowledge in Vision (2006)

    Google Scholar 

  44. Le Meur, O., Baccino, T.: Methods for comparing scanpaths and saliency maps: strengths and weaknesses. Behav. Res. Methods 45, 251–266 (2013)

    Article  Google Scholar 

  45. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  46. Ojala, T., Pietikäinen, M., Mäenpää, T.: A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. Adv. Pattern Recognit. 2013, 399–408 (2001)

    MATH  Google Scholar 

Download references

Acknowledgement

This work is part of the projects VisualNavDrone 2016-PIC-024 and MultiNavCar 2016-PIC-025, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.

Author information

Authors and Affiliations

  1. Dep. Seguridad y Defensa, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Wilbert G. Aguilar, Hugo Ruiz & Humberto Parra

  2. Dep. Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Marco A. Luna & Julio F. Moya

  3. CICTE Research Center, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Wilbert G. Aguilar

  4. GREC Research Group, Universitat Politècnica de Catalunya, Barcelona, Spain

    Wilbert G. Aguilar & Cecilio Angulo

  5. Universitat de Barcelona, Barcelona, Spain

    Vanessa Abad

  6. PLM Research Center, Purdue University, West Lafayette, IN, USA

    Hugo Ruiz

  7. Universidad Politécnica de Madrid, Madrid, Spain

    Humberto Parra

Authors
  1. Wilbert G. Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco A. Luna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julio F. Moya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vanessa Abad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hugo Ruiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Humberto Parra
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cecilio Angulo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wilbert G. Aguilar .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Aguilar, W.G. et al. (2017). Pedestrian Detection for UAVs Using Cascade Classifiers and Saliency Maps. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59147-6_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59146-9

  • Online ISBN: 978-3-319-59147-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation