Adaptive object detection algorithms for resource constrained autonomous robotic systems

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.