Method

Improved HHA Encoding and Two-branch Feature Fusion [la] [HHA-TFFEM]


Submitted on 2 Mar. 2022 11:11 by
Fang Tan (Electronic information college, Northwestern Polytechnical University)

Running time:0.14 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
First, The point cloud is encoded into an improved
HHA image, and then the HHA and RGB images are
fused
to detect pedestrians.
Parameters:
backbone=resnet50
basemodel=FasterRCNN
LR=0.01
epoch=16
Latex Bibtex:
@article{tan20223d,
title={3D Sensor Based Pedestrian Detection by
Integrating Improved HHA Encoding and Two-Branch
Feature Fusion},
author={Tan, Fang and Xia, Zhaoqiang and Ma,
Yupeng and Feng, Xiaoyi},
journal={Remote Sensing},
volume={14},
number={3},
pages={645},
year={2022},
publisher={Multidisciplinary Digital Publishing
Institute}
}

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) 87.01 % 78.53 % 74.70 %
This table as LaTeX

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




eXTReMe Tracker