Research Information System
Kuwait Journal of Science, vol.49, no.4, pp.1-9, 2022 (SCI-Expanded)
The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments. Hence, it is hard to ascertain these concealed targets. To address this issue, a novel deep neural network is proposed in this paper that produces an estimated mask within the hidden target for an input image. Our approach consists of two phases: hidden target segmentation and hidden target identification. For the first phase, we propose the Multilevel Attention Network (MA-Net), which generates the camouflaged target mask based on a Multi-Attention Module (MAM) that helps distinguish the hidden people from the background. Later on, the concealed target will be highlighted in the second phase. Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.