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2019 | OriginalPaper | Chapter

Pedestrian Detection and Trajectory Estimation in the Compressed Domain Using Thermal Images

Authors : Ichraf Lahouli, Zied Chtourou, Mohamed Ali Ben Ayed, Robby Haelterman, Geert De Cubber, Rabah Attia

Published in: Computer Vision, Imaging and Computer Graphics Theory and Applications

Publisher: Springer International Publishing

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Abstract

Since a few decades, the Unmanned Aerial Vehicles (UAVs) are considered precious tools for different military applications such as the automatic surveillance in outdoor environments. Nevertheless, the onboard implementation of image and video processing techniques poses many challenges like the high computational cost and the high bandwidth requirements, especially on low-performance processing platforms like small or medium UAVs. A fast and efficient framework for pedestrian detection and trajectory estimation for outdoor surveillance using thermal images is presented in this paper. First, the detection process is based on a conjunction between contrast enhancement techniques and saliency maps as a hotspot detector, on Discrete Chebychev Moments (DCM) as a global image content descriptor and on a linear Support Vector Machine (SVM) as a classifier. Second, raw H.264/AVC compressed video streams with limited computational overhead are exploited to estimate the trajectories of the detected pedestrians. In order to simulate suspicious events, six different scenarios were carried out and filmed using a thermal camera. The obtained results show the effectiveness and the low computational requirements of the proposed framework which make it suitable for real-time applications and onboard implementation.
Literature
1.
go back to reference Karakasis, E., Bampis, L., Amanatiadis, A., Gasteratos, A., Tsalides, P.: Digital elevation model fusion using spectral methods. In: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, pp. 340–345. IEEE (2014) Karakasis, E., Bampis, L., Amanatiadis, A., Gasteratos, A., Tsalides, P.: Digital elevation model fusion using spectral methods. In: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, pp. 340–345. IEEE (2014)
2.
go back to reference Davis, J.W., Keck, M.A.: A two-stage template approach to person detection in thermal imagery. In: Seventh IEEE Workshops on Application of Computer Vision, 2005, WACV/MOTIONS 2005, vol. 1, pp. 364–369, January 2005 Davis, J.W., Keck, M.A.: A two-stage template approach to person detection in thermal imagery. In: Seventh IEEE Workshops on Application of Computer Vision, 2005, WACV/MOTIONS 2005, vol. 1, pp. 364–369, January 2005
3.
go back to reference Torabi, A., Massé, G., Bilodeau, G.-A.: An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications. Comput. Vis. Image Underst. 116, 210–221 (2012) CrossRef Torabi, A., Massé, G., Bilodeau, G.-A.: An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications. Comput. Vis. Image Underst. 116, 210–221 (2012) CrossRef
4.
go back to reference Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions (MSER). Image Vis. Comput. 22(10), 761–767 (2004) CrossRef Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions (MSER). Image Vis. Comput. 22(10), 761–767 (2004) CrossRef
5.
go back to reference Tun, W.N., Tyan, M., Kim, S., Nah, S.-H., Lee, J.-W.: Marker tracking with AR.Drone for visual-based navigation using SURF and MSER algorithms. In: Korean Society for Aeronautical & Space Sciences Conference, pp. 124–125 (2017) Tun, W.N., Tyan, M., Kim, S., Nah, S.-H., Lee, J.-W.: Marker tracking with AR.Drone for visual-based navigation using SURF and MSER algorithms. In: Korean Society for Aeronautical & Space Sciences Conference, pp. 124–125 (2017)
6.
go back to reference Sun, X., Ding, J., Dalla Chiara, G., Cheah, L., Cheung, N.-M.: A generic framework for monitoring local freight traffic movements using computer vision-based techniques. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 63–68. IEEE (2017) Sun, X., Ding, J., Dalla Chiara, G., Cheah, L., Cheung, N.-M.: A generic framework for monitoring local freight traffic movements using computer vision-based techniques. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 63–68. IEEE (2017)
7.
go back to reference Kumar, A., Gupta, S.: Detection and recognition of text from image using contrast and edge enhanced MSER segmentation and OCR. IJOSCIENCE (Int. J. Online Sci.) 3(3), 07 (2017) Kumar, A., Gupta, S.: Detection and recognition of text from image using contrast and edge enhanced MSER segmentation and OCR. IJOSCIENCE (Int. J. Online Sci.) 3(3), 07 (2017)
8.
go back to reference Khosravi, M., Hassanpour, H.: A novel image structural similarity index considering image content detectability using maximally stable extremal region descriptor. Int. J. Eng. Trans. B Appl. 30(2), 172 (2017) Khosravi, M., Hassanpour, H.: A novel image structural similarity index considering image content detectability using maximally stable extremal region descriptor. Int. J. Eng. Trans. B Appl. 30(2), 172 (2017)
9.
go back to reference Alyammahi, S.M.R., Salahat, E.N., Saleh, H.H.M., Sluzek, A.S., Elnaggar, M.I.: Hardware architecture for linear-time extraction of maximally stable extremal regions (MSERs), 22 August 2017. US Patent 9,740,947 Alyammahi, S.M.R., Salahat, E.N., Saleh, H.H.M., Sluzek, A.S., Elnaggar, M.I.: Hardware architecture for linear-time extraction of maximally stable extremal regions (MSERs), 22 August 2017. US Patent 9,740,947
10.
go back to reference Śluzek, A.: MSER and SIMSER regions: a link between local features and image segmentation. In: Proceedings of the 2017 International Conference on Computer Graphics and Digital Image Processing, p. 15. ACM (2017) Śluzek, A.: MSER and SIMSER regions: a link between local features and image segmentation. In: Proceedings of the 2017 International Conference on Computer Graphics and Digital Image Processing, p. 15. ACM (2017)
11.
go back to reference Lu, T., Liu, R.: Detecting text in natural scenes with multi-level MSER and SWT. In: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), vol. 10615, p. 106150G. International Society for Optics and Photonics (2018) Lu, T., Liu, R.: Detecting text in natural scenes with multi-level MSER and SWT. In: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), vol. 10615, p. 106150G. International Society for Optics and Photonics (2018)
12.
go back to reference Zhang, X., Gao, X., Tian, C.: Text detection in natural scene images basedon color prior guided MSER. Neurocomputing 307, 61–71 (2018) CrossRef Zhang, X., Gao, X., Tian, C.: Text detection in natural scene images basedon color prior guided MSER. Neurocomputing 307, 61–71 (2018) CrossRef
13.
go back to reference Karim, S., Halepoto, I.A., Manzoor, A., Phulpoto, N.H., Laghari, A.A.: Vehicle detection in satellite imagery using maximally stable extremal regions. IJCSNS 18(4), 75 (2018) Karim, S., Halepoto, I.A., Manzoor, A., Phulpoto, N.H., Laghari, A.A.: Vehicle detection in satellite imagery using maximally stable extremal regions. IJCSNS 18(4), 75 (2018)
14.
go back to reference Ma, Y., Wu, X., Yu, G., Xu, Y., Wang, Y.: Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery. Sensors 16(4), 446 (2016) CrossRef Ma, Y., Wu, X., Yu, G., Xu, Y., Wang, Y.: Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery. Sensors 16(4), 446 (2016) CrossRef
15.
go back to reference Uemura, H., Ishikawa, S., Mikolajczyk, K.: Feature tracking and motion compensation for action recognition. In: BMVC, pp. 1–10 (2008) Uemura, H., Ishikawa, S., Mikolajczyk, K.: Feature tracking and motion compensation for action recognition. In: BMVC, pp. 1–10 (2008)
16.
go back to reference Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008) CrossRef Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008) CrossRef
17.
go back to reference Zhang, S., Zhang, L., Gao, R., Liu, C.: Mobile robot moving target detection and tracking system. In: Proceedings of the 2017 The 7th International Conference on Computer Engineering and Networks CENet2017), 22–23 July 2017, Shanghai, China (2017) Zhang, S., Zhang, L., Gao, R., Liu, C.: Mobile robot moving target detection and tracking system. In: Proceedings of the 2017 The 7th International Conference on Computer Engineering and Networks CENet2017), 22–23 July 2017, Shanghai, China (2017)
18.
go back to reference Sundari, V.K., Manikandan, M.: Real time implementation of surf based target tracking algorithm. Int. J. Intell. Electron. Syst. 11(1) (2017) Sundari, V.K., Manikandan, M.: Real time implementation of surf based target tracking algorithm. Int. J. Intell. Electron. Syst. 11(1) (2017)
19.
go back to reference Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013) Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013)
20.
go back to reference Wu, S., Oreifej, O., Shah, M.: Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1419–1426. IEEE (2011) Wu, S., Oreifej, O., Shah, M.: Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1419–1426. IEEE (2011)
21.
go back to reference Park, S.-M., Lee, J.: Object tracking in MPEG compressed video using mean-shift algorithm. In: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia, vol. 2, pp. 748–752. IEEE (2003) Park, S.-M., Lee, J.: Object tracking in MPEG compressed video using mean-shift algorithm. In: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia, vol. 2, pp. 748–752. IEEE (2003)
22.
go back to reference Babu, R.V., Ramakrishnan, K., Srinivasan, S.: Video object segmentation: a compressed domain approach. IEEE Trans. Circ. Syst. Video Technol. 14(4), 462–474 (2004) CrossRef Babu, R.V., Ramakrishnan, K., Srinivasan, S.: Video object segmentation: a compressed domain approach. IEEE Trans. Circ. Syst. Video Technol. 14(4), 462–474 (2004) CrossRef
23.
go back to reference Babu, R.V., Ramakrishnan, K.: Recognition of human actions using motion history information extracted from the compressed video. Image Vis. Comput. 22(8), 597–607 (2004) CrossRef Babu, R.V., Ramakrishnan, K.: Recognition of human actions using motion history information extracted from the compressed video. Image Vis. Comput. 22(8), 597–607 (2004) CrossRef
24.
go back to reference Yeo, C., Ahammad, P., Ramchandran, K., Sastry, S.S.: Compressed domain real-time action recognition. In: 2006 IEEE 8th Workshop on Multimedia Signal Processing, pp. 33–36, IEEE (2006) Yeo, C., Ahammad, P., Ramchandran, K., Sastry, S.S.: Compressed domain real-time action recognition. In: 2006 IEEE 8th Workshop on Multimedia Signal Processing, pp. 33–36, IEEE (2006)
26.
go back to reference Biswas, S., Babu, R.V.: H.264 compressed video classification using histogram of oriented motion vectors (HOMV). In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2040–2044. IEEE (2013) Biswas, S., Babu, R.V.: H.264 compressed video classification using histogram of oriented motion vectors (HOMV). In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2040–2044. IEEE (2013)
28.
go back to reference Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2593–2600 (2014) Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2593–2600 (2014)
29.
go back to reference Zhang, B., Wang, L., Wang, Z., Qiao, Y., Wang, H.: Real-time action recognition with enhanced motion vector CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2718–2726 (2016) Zhang, B., Wang, L., Wang, Z., Qiao, Y., Wang, H.: Real-time action recognition with enhanced motion vector CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2718–2726 (2016)
30.
go back to reference Poularakis, S., Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Efficient motion estimation methods for fast recognition of activities of daily living. Signal Process. Image Commun. 53, 1–12 (2017) CrossRef Poularakis, S., Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Efficient motion estimation methods for fast recognition of activities of daily living. Signal Process. Image Commun. 53, 1–12 (2017) CrossRef
31.
go back to reference Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Recognition of activities of daily living for smart home environments. In: 2013 9th International Conference on Intelligent Environments (IE), pp. 173–180. IEEE (2013) Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Recognition of activities of daily living for smart home environments. In: 2013 9th International Conference on Intelligent Environments (IE), pp. 173–180. IEEE (2013)
32.
go back to reference Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1597–1604 (2009) Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1597–1604 (2009)
33.
go back to reference Arodź, T., Kurdziel, M., Popiela, T.J., Sevre, E.O., Yuen, D.A.: Detection of clustered microcalcifications in small field digital mammography. Comput. Methods Programs Biomed. 81(1), 56–65 (2006) CrossRef Arodź, T., Kurdziel, M., Popiela, T.J., Sevre, E.O., Yuen, D.A.: Detection of clustered microcalcifications in small field digital mammography. Comput. Methods Programs Biomed. 81(1), 56–65 (2006) CrossRef
34.
go back to reference Lahouli, I., et al.: Hot spot method for pedestrian detection using saliency maps, discrete chebyshev moments and support vector machine. IET Image Process. 12, 1284–1291 (2018) CrossRef Lahouli, I., et al.: Hot spot method for pedestrian detection using saliency maps, discrete chebyshev moments and support vector machine. IET Image Process. 12, 1284–1291 (2018) CrossRef
35.
go back to reference Lahouli, I., Haelterman, R., Chtourou, Z., Cubber, G.D., Attia, R.: Pedestrian detection and tracking in thermal images from aerial mpeg videos. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, vol. 5, pp. 487–495. INSTICC, SciTePress (2018) Lahouli, I., Haelterman, R., Chtourou, Z., Cubber, G.D., Attia, R.: Pedestrian detection and tracking in thermal images from aerial mpeg videos. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, vol. 5, pp. 487–495. INSTICC, SciTePress (2018)
Metadata
Title
Pedestrian Detection and Trajectory Estimation in the Compressed Domain Using Thermal Images
Authors
Ichraf Lahouli
Zied Chtourou
Mohamed Ali Ben Ayed
Robby Haelterman
Geert De Cubber
Rabah Attia
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-26756-8_10

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