Method

DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [la] [DepthCN]
https://github.com/alirezaasvadi/Multimodal

Submitted on 16 Jun. 2018 23:11 by
Alireza Asvadi ()

Running time:2.3 s
Environment:GPU @ 3.5 Ghz (Matlab)

Method Description:
A vehicle detection system based on the
Hypothesis Generation and Verification
paradigms is proposed. The data inputted to the
system is a point cloud obtained from a 3D-
LIDAR mounted on board an instrumented vehicle,
which is then transformed to a dense-Depth Map
(DM). Specifically, the DBSCAN clustering is
used to extract structures (i.e., to segment
individual obstacles that stand on the ground)
from the 3D-LIDAR data to form class-agnostic
object hypotheses, followed by (class-specific)
ConvNet-based classification of such hypotheses
(in the form of a DM).
Parameters:
Grid size (0.5 m), Variance (0.01 m), Minimum # of
points (5) and Distance metric (0.5 m)
Latex Bibtex:
@inproceedings{asvadi2017depthcn,
title={DepthCN: vehicle detection using 3D-
LIDAR and convnet},
author={Asvadi, Alireza and Garrote,
Lu{\'\i}s and Premebida, Cristiano and Peixoto,
Paulo and Nunes, Urbano J},
booktitle={IEEE ITSC},
year={2017}
}

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
Car (Detection) 37.59 % 23.21 % 18.00 %
This table as LaTeX


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




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