Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective
Chiman Kwan, Jude Larkin
Abstract
Detection of small moving objects in long range infrared (IR) videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we present two unsupervised, modular, and flexible frameworks to detect small moving targets. The key idea was inspired by change detection (CD) algorithms where frame differences can help detect motions. Our frameworks consist of change detection, small target detection, and some post-processing algorithms such as image denoising and dilation. Extensive experiments using actual long range mid-wave infrared (MWIR) videos with target distances beyond 3500 m from the camera demonstrated that one approach, using Local Intensity Gradient (LIG) only once in the workflow, performed better than the other, which used LIG in two places, in a 3500 m video, but slightly worse in 4000 m and 5000 m videos. Moreover, we also investigated the use of synthetic bands for target detection and observed promising results for 4000 m and 5000 m videos. Finally, a comparative study with two conventional methods demonstrated that our proposed scheme has comparable performance.