Unmanned systems used for threat detection and identification are still not efficient enough for monitoring autonomously the battlefield. The limitation on size and energy makes those systems unable to use most state-of-the-art computer vision algorithms for recognition. The bio-inspired approach based on the humans peripheral and foveal visions has been reported as a way to combine recognition performance and computational efficiency. As a low resolution camera observes a large zone and detects significant changes, a second camera focuses on each event and provides a high resolution image of it. While such biomimetic existing approaches usually separate the two vision modes according to their functionality (e.g. detection, recognition) and to their basic primitives (i.e. features, algorithms), our approach uses common structures and features for both peripheral and foveal cameras, thereby decreasing the computational load with respect to the previous approaches. The proposed approach is demonstrated using simulated data. The outcome proves particularly attractive for real time embedded systems, as the primitives (features and classifier) have already proven good performances in low power embedded systems. This first result reveals the high potential of dual views fusion technique in the context of long duration unmanned video surveillance systems. It also encourages us to go further into miming the mechanisms of the human eye. In particular, it is expected that adding a retro-action of the fovea towards the peripheral vision will further enhance the quality and efficiency of the detection process.