Method

Exploring Multi-Branch and High-Level Semantic Networks for Improving Object Detection [MHN]


Submitted on 15 Jan. 2019 16:03 by
Jiale Cao (Tianjin)

Running time:0.39 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
we propose a multi-branch and high-level semantic
network by gradually splitting a base network into
multiple different branches.
Parameters:
TBD
Latex Bibtex:
@inproceedings{jiale2018arXiv,
title={Exploring Multi-Branch and High-Level
Semantic Networks for Improving Pedestrian
Detection},
author={Jiale Cao and Yanwei Pang and Xuelong Li},
booktitle={arXiv:1804.00872},
year={2018}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Pedestrian (Detection) 85.81 % 74.60 % 68.94 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot




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