Search Results for author: Mark Campbell

Found 25 papers, 15 papers with code

Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning

1 code implementation16 Dec 2023 Rohan Banerjee, Prishita Ray, Mark Campbell

Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving.

Autonomous Driving Bayesian Optimization +3

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

1 code implementation23 Oct 2023 Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.

3D Object Detection Colorization +4

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

1 code implementation27 Mar 2023 Yurong You, Cheng Perng Phoo, Katie Z Luo, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts.

3D Object Detection Autonomous Driving +2

Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs

no code implementations23 Sep 2022 Youya Xia, Josephine Monica, Wei-Lun Chao, Bharath Hariharan, Kilian Q Weinberger, Mark Campbell

In this paper, we investigate the idea of turning sensor inputs (i. e., images) captured in an adverse condition into a benign one (i. e., sunny), upon which the downstream tasks (e. g., semantic segmentation) can attain high accuracy.

Autonomous Driving Image-to-Image Translation +4

Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments

no code implementations27 Jul 2022 Vikram Shree, Sarah Allen, Beatriz Asfora, Jacopo Banfi, Mark Campbell

To address the first challenge, we propose to harness the enormous amount of visual content available in the form of movies and TV shows, and develop a dataset that can represent hazardous environments encountered in the real world.

Accelerated consensus in multi-agent networks via memory of local averages

no code implementations25 Sep 2021 Aditya Bhaskar, Shriya Rangarajan, Vikram Shree, Mark Campbell, Francesca Parise

There, the DeGroot update on the current states is followed by a linear combination with the previous states.

Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar

1 code implementation29 Mar 2021 Brian H. Wang, Carlos Diaz-Ruiz, Jacopo Banfi, Mark Campbell

We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector.

object-detection Object Detection

Interactive Natural Language-based Person Search

1 code implementation19 Feb 2020 Vikram Shree, Wei-Lun Chao, Mark Campbell

In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions.

Person Search Question Answering

An Empirical Study of Person Re-Identification with Attributes

1 code implementation25 Jan 2020 Vikram Shree, Wei-Lun Chao, Mark Campbell

Person re-identification aims to identify a person from an image collection, given one image of that person as the query.

Attribute Person Re-Identification

LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

1 code implementation30 Oct 2019 Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell

We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.

Image Segmentation Point Cloud Segmentation +2

Anytime Stereo Image Depth Estimation on Mobile Devices

3 code implementations26 Oct 2018 Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger

Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.

Stereo Depth Estimation

Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking

no code implementations19 Oct 2018 Brian H. Wang, Yan Wang, Kilian Q. Weinberger, Mark Campbell

We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appearances.

Person Re-Identification Translation

All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles

no code implementations7 May 2016 Peter Radecki, Mark Campbell, Kevin Matzen

A novel probabilistic perception algorithm is presented as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle in all-weather conditions.

Classification General Classification +2

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