Search Results for author: David J. Crandall

Found 20 papers, 9 papers with code

GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo

1 code implementation30 Oct 2023 Vibhas K. Vats, Sripad Joshi, David J. Crandall, Md. Alimoor Reza, Soon-Heung Jung

Traditional multi-view stereo (MVS) methods rely heavily on photometric and geometric consistency constraints, but newer machine learning-based MVS methods check geometric consistency across multiple source views only as a post-processing step.

3D Reconstruction Multi-View 3D Reconstruction +1

The Affective Growth of Computer Vision

no code implementations CVPR 2021 Norman Makoto Su, David J. Crandall

The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society.

Cultural Vocal Bursts Intensity Prediction

Stepwise Goal-Driven Networks for Trajectory Prediction

1 code implementation25 Mar 2021 Chuhua Wang, Yuchen Wang, Mingze Xu, David J. Crandall

We propose to predict the future trajectories of observed agents (e. g., pedestrians or vehicles) by estimating and using their goals at multiple time scales.

Decoder Multi-future Trajectory Prediction +1

Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification

5 code implementations ECCV 2020 Mang Ye, Jianbing Shen, David J. Crandall, Ling Shao, Jiebo Luo

In this paper, we propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.

Person Re-Identification Retrieval

Pose-Guided Knowledge Transfer for Object Part Segmentation

no code implementations1 Apr 2020 Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, David J. Crandall

Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive.

Object Segmentation +2

Unsupervised Traffic Accident Detection in First-Person Videos

3 code implementations2 Mar 2019 Yu Yao, Mingze Xu, Yuchen Wang, David J. Crandall, Ella M. Atkins

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.

Autonomous Driving Object Localization +4

Temporal Recurrent Networks for Online Action Detection

2 code implementations ICCV 2019 Mingze Xu, Mingfei Gao, Yi-Ting Chen, Larry S. Davis, David J. Crandall

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.

Online Action Detection

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

2 code implementations19 Sep 2018 Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, Behzad Dariush

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.

Autonomous Driving Decoder +2

Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos

no code implementations ECCV 2018 Mingze Xu, Chenyou Fan, Yuchen Wang, Michael S. Ryoo, David J. Crandall

In this paper, we wish to solve two specific problems: (1) given two or more synchronized third-person videos of a scene, produce a pixel-level segmentation of each visible person and identify corresponding people across different views (i. e., determine who in camera A corresponds with whom in camera B), and (2) given one or more synchronized third-person videos as well as a first-person video taken by a mobile or wearable camera, segment and identify the camera wearer in the third-person videos.

Segmentation

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

1 code implementation11 Jan 2018 Mingze Xu, Chenyou Fan, John D Paden, Geoffrey C. Fox, David J. Crandall

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical.

Structured Prediction Surface Reconstruction

Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition

no code implementations11 Jan 2018 Mingze Xu, Aidean Sharghi, Xin Chen, David J. Crandall

A major emerging challenge is how to protect people's privacy as cameras and computer vision are increasingly integrated into our daily lives, including in smart devices inside homes.

Action Recognition Temporal Action Localization

Automatic Estimation of Ice Bottom Surfaces from Radar Imagery

no code implementations21 Dec 2017 Mingze Xu, David J. Crandall, Geoffrey C. Fox, John D Paden

Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change.

Minimizing Supervision for Free-space Segmentation

1 code implementation16 Nov 2017 Satoshi Tsutsui, Tommi Kerola, Shunta Saito, David J. Crandall

Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments

Autonomous Driving Autonomous Navigation +3

A Study of Cross-domain Generative Models applied to Cartoon Series

no code implementations29 Sep 2017 Eman T. Hassan, David J. Crandall

We investigate Generative Adversarial Networks (GANs) to model one particular kind of image: frames from TV cartoons.

A Unified Model for Near and Remote Sensing

no code implementations ICCV 2017 Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs

To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.

Density Estimation

Identifying First-person Camera Wearers in Third-person Videos

no code implementations CVPR 2017 Chenyou Fan, Jang-Won Lee, Mingze Xu, Krishna Kumar Singh, Yong Jae Lee, David J. Crandall, Michael S. Ryoo

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene.

Activity Recognition Object Tracking +2

DeepDiary: Automatic Caption Generation for Lifelogging Image Streams

1 code implementation12 Aug 2016 Chenyou Fan, David J. Crandall

Lifelogging cameras capture everyday life from a first-person perspective, but generate so much data that it is hard for users to browse and organize their image collections effectively.

Caption Generation Image Captioning +2

Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

no code implementations ICCV 2015 Sven Bambach, Stefan Lee, David J. Crandall, Chen Yu

Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to.

Hand Detection Hand Segmentation

Multimodal Learning in Loosely-organized Web Images

no code implementations CVPR 2014 Kun Duan, David J. Crandall, Dhruv Batra

Photo-sharing websites have become very popular in the last few years, leading to huge collections of online images.

Metric Learning

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