Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

By Frédéric Suard, Alain Rakotomamonjy, Abdelaziz Bensrhair, Alberto Broggi. IEEE Intelligent Vehicles Symposium, 2006

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5fbb4925e5dc8b19046d0d4277b2b06a8dafce19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

People detection and tracking from aerial thermal views (2014, background): “However, the authors of [17] suggest that training with low resolution thermal images can still result in a robust classifier, which leads to the conclusion that the viewpoint angle differences are more substantial to the performance decrease.”
Pedestrian Protection Systems: Issues, Survey, and Challenges (2007, methodology): “They are used for pedestrian detection in conjunction with features such as Haar wavelets [74], sparse Gabor filters [17], image regions corresponding to hot spots in thermal IR images [102], and histograms of oriented gradients [86].”
Thermal cameras and applications: a survey (2013, methodology): “[ 173] and [112] do also use HOG detectors and SVM classiﬁer for pedestrian detection.”
A Survey of Recent Advances in Face Detection (2010, background): “Later, Dalal and Triggs [13] proposed a similar scheme called histogram of oriented gradients (HoG), which became a very popular feature for human/pedestrian detection [120, 25, 88, 43, 15].”
A survey on face detection in the wild: Past, present and future (2015, background): “Later, in [19] a similar scheme called histogram of oriented gradients (HoG) was proposed, which became a very popular feature for human/pedestrian detection [81] [82] [83] [84] [85] (we will discuss about the use of HoG features in face detection in the next subsection).”
Survey of Pedestrian Detection for Advanced Driver Assistance Systems (2010, methodology): “Some implementations include single thresholding [43], double image and hot-spots-based thresholding [44], and adaptive intensitybased thresholding [45], [46].”
Face recognition using Histograms of Oriented Gradients (2011, background): “…used in many different problems in computer vision, such as pedestrian detection (Bertozzi et al., 2007; Wang and Lien, 2007; Chuang et al., 2008; Watanabe et al., 2009; Baranda et al., 2008; He et al., 2008; Kobayashi et al., 2008; Suard et al., 2006; Zhu et al., 2006; Perdersoli et al., 2007a,b).”
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery (2013, methodology): “The methods applied to thermal imaging span from simple thresholding and shape analysis [43, 17, 39, 15, 7] to more complex, but well-known methods such as HOG and SVM [42, 37, 41, 31, 26] as well as contour analysis [10, 9, 27, 38].”