Decades of ongoing research have shown that background modelling is a very powerful technique, which is used in intelligent surveillance systems, in order to extract features of interest, known as foregrounds. In order to work with the dynamic nature of different scenes, many techniques of background modelling adopted the unsupervised approach of Gaussian Mixture Model with an iterative paradigm. Although the technique has had much success, a problem occurs in cases of sudden scene changes with high variation (e.g., illumination changes, camera jittering) that the model unknowingly and unnecessarily takes into account those effects and distorts the results. Therefore, this paper proposes an unsupervised, parallelized, and tensor-based approach that algorithmically works with entropy estimations. These entropy estimations are used in order to assess the uncertainty level of a constructed background, which predicts both the present and future variations from the inputs, thereby opting to use either the incoming frames to update the background or simply discard them. Our experiments suggest that this method is highly integrable into a surveillance system that consists of other functions and can be competitive with state-of-the-art methods in terms of processing speed.

Background Subtraction (BgS) is a widely researched technique to develop online Change Detection algorithms for static video cameras. Many BgS methods have employed the unsupervised, adaptive approach of Gaussian Mixture Model (GMM) to produce decent backgrounds, but they lack proper consideration of scene semantics to produce better foregrounds. On the other hand, with considerable computational expenses, BgS with Deep Neural Networks (DNN) is able to produce accurate background and foreground segments. In our research, we blend both approaches for the best. First, we formulated a network called Convolutional Density Approximation (CDA) for direct density estimation of background models. Then, we propose a self-supervised training strategy for CDA to adaptively capture high-frequency color distributions for the corresponding backgrounds. Finally, we show that background models can indeed assist foreground extraction by an efficient Neural Motion Subtraction (NeMos) network. Our experiments verify competitive results in the balance between effectiveness and efficiency.