FPGA Implementation of Multi-scale Pedestrian Detection in Thermal Images

• Autorzy: Kańka T. , Kryjak T. , Gorgon M.

Identyfikatory

DOI

10.1515/ipc-2016-0016

• Języki publikacji: EN

Abstrakty

EN

In this paper an embedded vision system for human silhouette detection in thermal images is presented. As the computing platform a reprogrammable device (FPGA – Field Programmable Gate Array) is used. The detection algorithm is based on a sliding window approach, which content is compared with a probabilistic template. Moreover, detection is four scales in supported. On the used test database, the proposed method obtained 97% accuracy, with average one false detection per frame. Due to the used parallelization and pipelining real-time processing for 720 × 480 @ 50 fps and 1280 × 720 @ 50 fps video streams was achieved. The system has been practically verified in a test setup with a thermal camera.

Słowa kluczowe

EN

pedestrian detection; thermal images; FPGA; multi-scale image processing; embedded vision system; real-time image processing

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2016
• Tom: Vol. 21, no. 3
• Strony: 55--68
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Kańka T.

• AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków

autor

Kryjak T.

• AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków

autor

Gorgon M.

• AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków

Bibliografia

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• [4] Dalal, N., Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE
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• [6] Geronimo, D., Lopez, A.M., Sappa, A.D., Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE transactions on pattern analysis and machine intelligence, 32(7), 1239-1258
• [7] Hurney, P., Waldron, P., Morgan, F., Jones, E., Glavin, M. (2015). Review of pedestrian detection techniques in automotive far-infrared video. IET intelligent transport systems, 9(8), 824-832
• [8] Lakshmi, A., Faheema, A.G.J., Deodhare, D. (2016). Pedestrian detection in thermal images: An automated scale based region extraction with curvelet space validation. Infrared Physics & Technology, 76, 421-438
• [9] Nanda, H., Davis, L. (2002, June). Probabilistic template based pedestrian detection in infrared videos. In Intelligent Vehicle Symposium, 2002. IEEE (Vol. 1, pp. 15-20). IEEE
• [10] Negied, N.K., Hemayed, E.E., Fayek, M. B. (2015). Pedestrians’ detection in thermal bands-Critical survey. Journal of Electrical Systems and Information Technology, 2(2), 141-148
• [11] Walczyk, R. (2013). Hardware architectures for infrared pedestrian detection systems
• [12] Walczyk, R., Armitage, A., Binnie, T.D. (2009). An embedded real-time pedestrian detection system using an infrared camera
• [13] Walczyk, R., Balazs, A., Armitage, A., Binnie, T.D. (2011). System architectures for infrared pedestrian tracking
• [14] Xiao, H., Song, H., He, W., Yuan, K. (2015, August). Real-time shape and pedestrian detection with FPGA. In Mechatronics and Automation (ICMA), 2015 IEEE International Conference on (pp. 2381-2386). IEEE

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).