In this section
@inproceedings{Liu:2017:10.1109/ICDSP.2017.8096045,
author = {Liu, T and Stathaki, T},
doi = {10.1109/ICDSP.2017.8096045},
publisher = {IEEE},
title = {Enhanced pedestrian detection using deep learning based semantic image segmentation},
url = {http://dx.doi.org/10.1109/ICDSP.2017.8096045},
year = {2017}
}
TY - CPAPER
AB - Pedestrian detection and semantic segmentation arehighly correlated tasks which can be jointly used for betterperformance. In this paper, we propose a pedestrian detectionmethod making use of semantic labeling to improve pedestriandetection results. A deep learning based semantic segmentationmethod is used to pixel-wise label images into 11 common classes.Semantic segmentation results which encodes high-level imagerepresentation are used as additional feature channels to beintegrated with the low-level HOG+LUV features. Some falsepositives, such as falsely detected pedestrians located on a tree,can be easier eliminated by making use of the semantic cues.Boosted forest is used for training the integrated feature channelsin a cascaded manner for hard negatives mining. Experimentson the Caltech-USA pedestrian dataset show improvements ondetection accuracy by using the additional semantic cues.
AU - Liu,T
AU - Stathaki,T
DO - 10.1109/ICDSP.2017.8096045
PB - IEEE
PY - 2017///
TI - Enhanced pedestrian detection using deep learning based semantic image segmentation
UR - http://dx.doi.org/10.1109/ICDSP.2017.8096045
UR - https://ieeexplore.ieee.org/document/8096045
UR - http://hdl.handle.net/10044/1/49802
ER -
