A high-performance approach to detecting small targets in long-range low-quality infrared videos

Abstract

Since targets are small in long range infrared (IR) videos, it is challenging to accurately detect targets in those videos. In this paper, we propose a high performance approach to detecting small targets in long range and low quality infrared videos. Our approach consists of a video resolution enhancement module, a proven small target detector based on local intensity and gradient (LIG), a connected component (CC) analysis module, and a track association module to connect detections from multiple frames. Extensive experiments using actual mid-wave infrared (MWIR) videos in ranges between 3500 m and 5000 m from a benchmark dataset clearly demonstrated the efficacy of the proposed approach.

Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

“…Third, if one needs to implement LIG in hardware, then Here, we compare the performance of the proposed algorithm (standard workflow containing the CC change detection method) with two other conventional algorithms. One conventional algorithm is based on frame by frame detection [7] and the other one is based on optical flow [34]. Details can be found in [7,34].…”

“…It should be noted that the aforementioned papers detect targets frame by frame. Parallel to the above small target detection activities, there are some conventional target tracking methods [7,8] for videos. In general, target detection performance in videos can yield better results because target motion can be exploited.…”

“…In general, target detection performance in videos can yield better results because target motion can be exploited. For instance, the paper [7] combines single frame detection with a track fusion algorithm to yield improved target detection in infrared videos. Furthermore, various target detection and classification schemes for optical and infrared videos have been proposed in the literature .…”

“…In this research, we focus on small moving target detection in long range infrared videos where the ranges are 3500 m and beyond. In the literature, we have not seen target detection studies for such long ranges before except papers written by us [7,[9][10][11]34]. We propose two approaches based on change detection (CD) techniques for target detection in videos containing moving targets.…”

A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms

“…In recent research [24], two video super-resolution algorithms were compared. The Zoom Slow-Motion (ZSM) Algorithm [22] for video super-resolution performed better than the Dynamic Upsampling Filter (DUF) approach [17].…”