Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Abstract

Unmanned Aerial Vehicles (UAVs) are getting more and more uses in recent times. However, low-cost commercial UAVs may not possess enough computational power to run state of the art algorithms in order to perform certain tasks, negatively affecting performance. Remote computational systems, where heavy processing tasks can be offloaded emerge as a solution. However, they introduce latency, which can be undesirable for real-time tasks. Furthermore, if the task is simple, using a local algorithm with worse performance may be acceptable to avoid latency. As such, a method to decide which algorithm to use is of great importance. We consider the use case of computer vision tasks, in particular detection and tracking. In these tasks, image properties such as brightness, contrast, motion blur and clutter affect the algorithm performance. Our proposed methods use a combination of neural networks and kernel machines to estimate the performance of the algorithm given the input image. An appropriate cost function is then used to identify the best algorithm for the task given the input image, the task deadline, and the uncertainty in the variables of the algorithm, in particular computing time and error rate. Results show that our method matches or outperforms similar state of the art methods, complying with time restrictions while delivering increased performance.