UAS-Based Object Tracking via Deep Learning

Document Type

Conference Proceeding

Publication Date

3-14-2019

Publication Title

2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)

Publisher

AIS

First page number:

271

Last page number:

275

Abstract

Unmanned Aerial System-based object tracking is a challenging new task in the computer vision community. In addition, existing benchmark and research focus on short sequences that are less than a minute long. In this work, we show the limitations of state-of-the art trackers in front of long-term aerial tracking. We propose a novel long-term, real-time, intelligent system for unmanned aerial system -based vehicle tracking utilizing deep learning techniques. We integrate a fast and accurate correlation filter with the expressiveness of a convolutional neural network embedding. A re-initialization policy based on a real-time anomaly detection on correlation map combined with a one-shot detector ensure that our system is impervious to drift and occlusion.

Keywords

Unmanned aerial system; Unmanned aerial vehicles; Tracking; Object detection; Long-term; Deep learning

Disciplines

Electrical and Computer Engineering | Engineering | Other Electrical and Computer Engineering

Language

English

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