This study suggests effective noise reduction methods for wearable neckband devices, which are able to monitor users’ rear-view areas. The wearable neckband device helps the user to monitor rear-view areas in which he/she is unable to see in normal ways (without turning back). Unlike general computers or supercomputer systems, the neckband devices have some particular constraints such as small size, lightweight and low power consumption. In a general vision system, there are many kinds of noises, which significantly decrease system quality such as impulse noise, random noise, motion noise, etc. These noises also affect wearable devices, which use cameras as the system input. Moreover, when the user walks or runs, the neckband device moves accordingly. The changing position of the neckband device causes many other noises such as camera motion (ego-motion) noises. Furthermore, when the user walks from indoors to outdoors or vice versa, the illumination dramatically changes, which also affects the device performance. Effective noise reduction methods to deal with these noises are proposed in this study. Random noise and other small noises are removed by using a Gaussian filter and adaptive color threshold techniques. We propose to use feature detection and the homography matrix estimation method to reduce ego-motion noise. Remaining noises are cancelled out by a morphology technique. Finally, we apply Local Binary Patterns (LBP) descriptor and Adaboost classifier to classify whether there are people or not in the moving foreground object regions. The experiments demonstrate that our proposed noise reduction methods have achieved successful results in the different environments and users’ walking speeds.
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