Mr. Austin Welch Profile

Austin Welch

at Amazon Web Services Inc

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 16 June 2023 Presentation + Paper

Towards masked autoencoding pre-training for wide area motion imagery

Steve Goley, Rohan Pradhan, Austin Welch

Proceedings Volume 12525, 1252508 (2023) https://doi.org/10.1117/12.2665871

KEYWORDS: Object detection, Data modeling, Transformers, Remote sensing, Video, Computer vision technology

Read Abstract +

Transformer models are demonstrating remarkable and emergent capabilities in the natural language processing domain. These models are bounded only by the availability of large training datasets. These datasets can be tractably obtained since natural language models are pre-trained using self-supervision in the form of token masking. Papers like He et al. and Cao et al. have recently shown the power of this token masking technique by utilizing masked autoencoders as scalable vision learners in combination with a self-supervised pre-training technique for vision transformer models. Feichtenhofer et al. extended these techniques to video, proving that masked autoencoders are scalable spatiotemporal learners as well. To our best knowledge, these techniques have only been experimented on ground-level, object-centric style imagery and video. Extending these techniques to remote or overhead imagery presents two significant problems. First, the size of objects of interest are small compared to the typical mask patch size. Second, the frames are not object centered. In this study, we explore if modern self-supervised pre-training techniques like masked auto encoding extend well to overhead wide area motion imagery (WAMI) data. We argue that modern pre-training techniques like MAE are well suited to WAMI data given the typical object size in this domain as well as the ability to leverage strong global spatial contextual information. To this end, we conduct a comprehensive exploration of different patch sizes and masking ratios on the popular WAMI dataset, WPAFB 2009. We find that domain-specific adjustments to these pre-training techniques result in downstream performance improvements on computer vision tasks including object detection.

Proceedings Article | 16 June 2023 Presentation + Paper

TrackFuse: improving tracker performance by late stage fusion

Rohan Pradhan, Steve Goley, Austin Welch

Proceedings Volume 12525, 1252509 (2023) https://doi.org/10.1117/12.2665872

KEYWORDS: Object detection, Motion models, Mathematical optimization, Computer vision technology, Detection and tracking algorithms

Read Abstract +

Multiple object tracking (MOT) is a common computer vision problem that focuses on detecting objects and maintaining their identities through a sequence of image frames. Until now, there have been three main approaches to improve MOT performance: 1) improving the detector’s quality, 2) improving the tracker’s quality, or 3) creating novel approaches to jointly model detection and tracking. In this work, we argue that there is a fourth, simpler way to improve MOT performance, by fusing multiple multiple object trackers together. In this paper, we introduce a novel approach, TrackFuse, that aims to fuse the final tracks from two different models into a single output, similar to classification ensembling or weighted box fusion for object detection. The fundamental assumption of TrackFuse is that multiple trackers will fail uniquely, and similarly, multiple detectors will fail uniquely too. Thus, by fusing the output of multiple approaches to MOT, we can improve tracking performance. We test our approach on combinations of several high performing approaches to tracking and show state-of-the-art results on the MOTA metric on a held out validation set of the MOT17 dataset, compared to individual tracking models. Furthermore, we consistently show that fusing multiple object trackers provides a performance boost on multiple metrics compared to results of individual model outputs sent for fusion. Our code will be released soon.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years