The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements. Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points. Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart camera in industrial systems.

The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements. Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points. Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart camera in industrial systems.