In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

Object detection continues to play a significant part in computer vision theory, study and
practical application. Conventional object detection algorithms were primarily derived from …

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast
multi-scale object detection. The MS-CNN consists of a proposal sub-network and a …

The goal of this paper is to perform 3D object detection in single monocular images in the
domain of autonomous driving. Our method first aims to generate a set of candidate class …

In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for
many-task vehicle analysis from a given image. A robust convolutional network is introduced …

In this paper, we investigate two new strategies to detect objects accurately and efficiently
using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise …

The goal of this paper is to generate high-quality 3D object proposals in the context of
autonomous driving. Our method exploits stereo imagery to place proposals in the form of …

The goal of this paper is to perform 3D object detection in the context of autonomous driving.
Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo …

Vision-based vehicle detection approaches achieve incredible success in recent years with
the development of deep convolutional neural network (CNN). However, existing CNN …

In Convolutional Neural Network (CNN)-based object detection methods, region proposal
becomes a bottleneck when objects exhibit significant scale variation, occlusion or …