Contextual Boost for Pedestrian Detection

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Contextual Boost for Pedestrian Detection
Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In this paper we present that, besides the local features, context cues in the neighborhood provide important constraints that are not yet well utilized. We propose a framework to incorporate the context constraints for detection. First, we combine the local window with neighborhoodwindows to construct a multi-scale image context descriptor, designed to represent the contextual cues in spatial, scaling, and color spaces. Second, we develop an iterative classification algorithm called contextual boost. At each iteration, the classifier responses from the previous iteration across the neighborhood and multiple image scales, called classification context, are incorporated as additional features to learn a new classifier. The number of iterations is determined in the training process when the error rate converges. ...
Yuanyuan Ding, Jing Xiao
Added 05 Sep 2012
Updated 05 Sep 2012
Type Conference
Year 2012
Where CVPR
Authors Yuanyuan Ding, Jing Xiao
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