  This paper presents static object detection and segmentation method in videos
from cluttered scenes. Robust static object detection is still challenging task
due to presence of moving objects in many surveillance applications. The level
of difficulty is extremely influenced by on how you label the object to be
identified as static that do not establish the original background but appeared
in the video at different time. In this context, background subtraction
technique based on the frame difference concept is applied to the
identification of static objects. Firstly, we estimate a frame differencing
foreground mask image by computing the difference of each frame with respect to
a static reference frame. The Mixture of Gaussian MOG method is applied to
detect the moving particles and then outcome foreground mask is subtracted from
frame differencing foreground mask. Pre-processing techniques, illumination
equalization and de-hazing methods are applied to handle low contrast and to
reduce the noise from scattered materials in the air e.g. water droplets and
dust particles. Finally, a set of mathematical morphological operation and
largest connected-component analysis is applied to segment the object and
suppress the noise. The proposed method was built for rock breaker station
application and effectively validated with real, synthetic and two public data
sets. The results demonstrate the proposed approach can robustly detect,
segmented the static objects without any prior information of tracking.
