Adaptive object detection algorithms for resource constrained autonomous robotic systems

Joe Pappas; Venkat R. Dasari; Billy E. Geerhart; David M. Alexander; Peng Wang; Somali Chaterji

doi:10.1117/12.3013781

6 June 2024 Adaptive object detection algorithms for resource constrained autonomous robotic systems

Joe Pappas, Venkat R. Dasari, Billy E. Geerhart, David M. Alexander, Peng Wang, Somali Chaterji

Proceedings Volume 13058, Disruptive Technologies in Information Sciences VIII; 130580C (2024) https://doi.org/10.1117/12.3013781
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

We optimized and deployed the adaptive framework Virtuoso that can maintain real-time object detection even when experiencing high contention scenarios. The original Virtuoso framework uses an adaptive algorithm for the detection frame followed by a low-cost algorithm for the tracker frame which uses down-sampled images to reduce computation. One of our optimizations include detaching the single synchronous thread for detection and tracking into two parallel threads. This multi-threaded implementation allows for computationally high-cost detection algorithms to be used while still maintaining real-time output from the tracker thread. Another optimization we developed uses multiple down-sampled images to initialize each tracker based on the size of the input box; the multiple down-sampled images allow each tracker to choose the optimal image size for the box that it is tracking rather than a single down-sampled image being used for all trackers.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Joe Pappas, Venkat R. Dasari, Billy E. Geerhart, David M. Alexander, Peng Wang, and Somali Chaterji "Adaptive object detection algorithms for resource constrained autonomous robotic systems", Proc. SPIE 13058, Disruptive Technologies in Information Sciences VIII, 130580C (6 June 2024); https://doi.org/10.1117/12.3013781

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