Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine. Issue 7 (1st July 2018)

• Record Type: Journal Article
• Authors: Lahouli, Ichraf; Karakasis, Evangelos; Haelterman, Robby; Chtourou, Zied; De Cubber, Geert; Gasteratos, Antonios; Attia, Rabah
• Is Part Of: IET image processing. Volume 12:Issue 7(2018)
• Page Start: 1284
• Page End: 1291
• Publication Date: 2018-07-01
• Subjects: pedestrians -- support vector machines -- reliability -- video surveillance -- cameras -- image sensors -- temperature sensors -- temperature measurement -- infrared imaging -- image enhancement -- image classification; hot spot method -- pedestrian detection -- saliency map -- discrete Chebyshev moment -- border intrusion -- surveillance camera -- data streaming -- thermal sensor -- thermal signature -- thermal imaging -- contrast‐enhancement technique -- shape description -- support vector machine classifier -- image classification -- maximally stable extremal region detector; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0221
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The increasing risks of border intrusions or attacks on sensitive facilities and the growing availability of surveillance cameras lead to extensive research efforts for robust detection of pedestrians using images. However, the surveillance of borders or sensitive facilities poses many challenges including the need to set up many cameras to cover the whole area of interest, the high bandwidth requirements for data streaming and the high‐processing requirements. Driven by day and night capabilities of the thermal sensors and the distinguished thermal signature of humans, the authors propose a novel and robust method for the detection of pedestrians using thermal images. The method is composed of three steps: a detection which is based on a saliency map in conjunction with a contrast‐enhancement technique, a shape description based on discrete Chebyshev moments and a classification step using a support vector machine classifier. The performance of the method is tested using two different thermal datasets and is compared with the conventional maximally stable extremal regions detector. The obtained results prove the robustness and the superiority of the proposed framework in terms of true and false positives rates and computational costs which make it suitable for low‐performance processing platforms and real‐time applications.