  Object movement identification is one of the most researched problems in the
field of computer vision. In this task, we try to classify a pixel as
foreground or background. Even though numerous traditional machine learning and
deep learning methods already exist for this problem, the two major issues with
most of them are the need for large amounts of ground truth data and their
inferior performance on unseen videos. Since every pixel of every frame has to
be labeled, acquiring large amounts of data for these techniques gets rather
expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic
Distribution Neural Network (ADNN) for universal background subtraction which
utilizes probability information from the histogram of temporal pixels and
achieves promising results. Building onto this work, we developed an
intelligent video surveillance system that uses ADNN architecture for motion
detection, trims the video with parts only containing motion, and performs
anomaly detection on the trimmed video.
