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Sensors 2017, 17(11), 2699;

Pedestrian Detection with Semantic Regions of Interest

1,2,3,4,* , 1,2
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
University of Chinese Academy of Sciences, Beijing 110049, China
Author to whom correspondence should be addressed.
Received: 1 October 2017 / Revised: 16 November 2017 / Accepted: 16 November 2017 / Published: 22 November 2017
(This article belongs to the Section Physical Sensors)
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For many pedestrian detectors, background vs. foreground errors heavily influence the detection quality. Our main contribution is to design semantic regions of interest that extract the foreground target roughly to reduce the background vs. foreground errors of detectors. First, we generate a pedestrian heat map from the input image with a full convolutional neural network trained on the Caltech Pedestrian Dataset. Next, semantic regions of interest are extracted from the heat map by morphological image processing. Finally, the semantic regions of interest divide the whole image into foreground and background to assist the decision-making of detectors. We test our approach on the Caltech Pedestrian Detection Benchmark. With the help of our semantic regions of interest, the effects of the detectors have varying degrees of improvement. The best one exceeds the state-of-the-art. View Full-Text
Keywords: pedestrian detection; deep learning; background vs. foreground errors; semantic regions of interest pedestrian detection; deep learning; background vs. foreground errors; semantic regions of interest

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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He, M.; Luo, H.; Chang, Z.; Hui, B. Pedestrian Detection with Semantic Regions of Interest. Sensors 2017, 17, 2699.

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