Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing

Kaipei Yang; Aria Pezeshk; Charles Lehman; Amin Arbabian

doi:10.1117/12.3013107

7 June 2024 Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing

Kaipei Yang, Aria Pezeshk, Charles Lehman, Amin Arbabian

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 1305706 (2024) https://doi.org/10.1117/12.3013107
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Centralized fusion ensures minimal information loss and maximizes the effectiveness of state estimation. Statistically, it is the optimal solution for all sensor fusion configurations. In this paper, we introduce a local-sensor-driven asynchronous low-level centralized fusion methodology that seamlessly integrates radar and camera data at the level of detections from each sensor. For a local-sensor-driven asynchronous system, detections from the two sensing modalities with different sampling rates are transmitted to a centralized filter, which is updated whenever it receives a measurement. We implemented the proposed algorithm and validated the results using real data from manufacturing and industrial work sites. The data was obtained by Plato System’s Argus perception system, which combines high-resolution imaging mm-wave radar with camera sensors to provide indoor and outdoor activity tracking. We further compare the fusion results with vision-only MOT, as well as track-level fusion (track-to-track fusion).

Citation Download Citation

Kaipei Yang, Aria Pezeshk, Charles Lehman, and Amin Arbabian "Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 1305706 (7 June 2024); https://doi.org/10.1117/12.3013107

ACCESS THE FULL ARTICLE

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