Application Of ADNN For Background Subtraction In Smart Surveillance System
This work addresses the need for efficient motion detection in surveillance systems, but it is incremental as it builds on an existing ADNN method without introducing new algorithmic advancements.
The paper tackles the problem of background subtraction for object movement identification in smart surveillance by applying an existing Arithmetic Distribution Neural Network (ADNN) to detect motion, trim videos to motion-containing parts, and perform anomaly detection, achieving promising results as reported in prior work.
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.