Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

Chiman Kwan,Bence Budavari

doi:10.3390/rs12244024

Abstract

The detection of small moving objects in long-range infrared videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we summarize the investigation of efficient ways to enhance the performance of small target detection in long-range and low-quality infrared videos containing moving objects. In particular, we focus on unsupervised, modular, flexible, and efficient methods for target detection performance enhancement using motion information extracted from optical flow methods. Three well-known optical flow methods were studied. It was found that optical flow methods need to be combined with contrast enhancement, connected component analysis, and target association in order to be effective for target detection. Extensive experiments using long-range mid-wave infrared (MWIR) videos from the Defense Systems Information Analysis Center (DSIAC) dataset clearly demonstrated the efficacy of our proposed approach.

Highlights

Infrared videos in ground-based imagers contain a lot of background clutter and flickering noise due to air turbulence, sensor noise, etc
Some target detection and classification schemes using deep learning algorithms such as You Only Look Once (YOLO) for larger objects in short-range optical and infrared videos have been proposed in the literature [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]
The performance is reasonable for short ranges up to 2000 m in some videos, the performance dropped quite considerably in long ranges where the target sizes are so small

Summary

Introduction

Infrared videos in ground-based imagers contain a lot of background clutter and flickering noise due to air turbulence, sensor noise, etc. Some target detection and classification schemes using deep learning algorithms such as You Only Look Once (YOLO) for larger objects in short-range optical and infrared videos have been proposed in the literature [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. The performance is reasonable for short ranges up to 2000 m in some videos, the performance dropped quite considerably in long ranges where the target sizes are so small This is because YOLO uses texture information to help the detection. Real-time issues have been discussed in [21]

Objectives

Results

Conclusion