Pedestrian Detection from a Moving Vehicle

By Dariu M. Gavrila. European Conference on Computer Vision, 2000

529 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/999f7f5bee368864c3887a7237b45bec50deaa3d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Background-subtraction using contour-based fusion of thermal and visible imagery (2007, methodology): “Some of these methods (e.g., [20] [19]) have been tested with thermal imagery, but the limited nature of the examples examined does not provide a comprehensive evaluation.”
OBJ CUT (2005, methodology): “Edge orientation is included by computing the chamfer score only for edges with similar orientation, in order to make the distance function more robust [10].”
Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors (2005, background): “’s edge templates [7][8] locate humans by recognizing the fullbody pattern.”
A Two-Stage Template Approach to Person Detection in Thermal Imagery (2005, methodology): “Some examples include the direct use of wavelet features with support vector machines [6], coarse-to-ﬁne edge template matching [3], motion/intensity AdaBoosted classiﬁers [7], and the size/shape of image differencing regions [4].”
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors (2004, methodology): “Matching shape templates with the Chamfer distance has also been successfully used for pedestrian detection [1,5].”
Pedestrian detection in crowded scenes (2005, methodology): “Previous approaches to pedestrian detection have used either global models, e.g. using full-body appearance [20] or silhouettes [11] [10] [8]; or an assembly of local feature [23] or part detectors [18] [17].”
Shape context and chamfer matching in cluttered scenes (2003, background): “Gavrila [10] uses chamfer matching to detect pedestrian shapes in real time.”
Recent advances and trends in visual tracking: A review (2011, background): “Gavrila [16] presented a contour based hierarchical chamfer matching detector for pedestrian detection.”
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors (2007, unknown): “…et al. (1998) is known as the MIT pedestrian sample set which is available online 1 ), Felzen-szwalb’s shape models (Felzenszwalb, 2001), Wu et al.’s Markov Random Field based representation (Wu et al., 2005), and Gavrila et al.’s edge templates (Gavrila and Philomin, 1999; Gavrila, 2000).”
Tracking People by Learning Their Appearance (2007, background): “Algorithms that track people by template matching follow this approach [5], [13], [19], [20], [21], [25].”
From Trolley to Autonomous Vehicle: Perceptions of Responsibility and Moral Norms in Traffic Accidents with Self-Driving Cars (2016, background): “, [27]), while it is still conceivable that such systems may fail due to the high variability of contexts in which the system needs to function [28].”
Multiresolution Models for Object Detection (2010, background): “There is a quite large literature on pedestrian detection, dating back to the early scanning-window classifers of [9, 12, 13].”
Artificial vision in road vehicles (2002, methodology): “Another algorithm developed within the UTA project at DaimlerChrysler [56] presents a two-step approach where both phases rely on shape and pattern analysis.”
Real-time human pose recognition in parts from single depth images (2011, methodology): “While hierarchical matching [14] is faster, one would still need a massive exemplar set to achieve comparable accuracy.”
Model-based hand tracking using a hierarchical Bayesian filter (2006, methodology): “A key s uggestion was that multiple templates could be dealt with efficiently by building a tree of te mplates [15, 31].”