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Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features
In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster.
Semantic Channels for Fast Pedestrian Detection
However most of the top performing approaches provide state of art results at high computational costs.
Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier
By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.
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