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.
Highlights
In long range surveillance, targets may have around 10 or even fewer pixels and these are known as small targets
In addition to the above studies, we investigated the use of Extended Morphological Attribute Profile (EMAP) [38,39,40,41,42] and local contrast enhancement (LCE) [43] to synthesize multiple bands out of the single infrared image
ACD is based on an anomalous change detection framework that is applied to the Gaussian model
Summary
Targets may have around 10 or even fewer pixels and these are known as small targets. In a recent paper [34], optical flow techniques were applied to small target detection in long range infrared videos. Extensive experiments using three long range infrared videos demonstrated that the performance of the standard approach is better than the alternate approach. In addition to the above studies, we investigated the use of Extended Morphological Attribute Profile (EMAP) [38,39,40,41,42] and local contrast enhancement (LCE) [43] to synthesize multiple bands out of the single infrared image The motivation for this is that, in our recent change detection applications [44,45], we noticed remarkable improvement in change detection and target detection performance when EMAP was used.
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