Unsupervised Adversarial Learning for Dynamic Background Modeling

Dynamic Background Modeling (DBM) is a crucial task in many computer vision based applications such as human activity analysis, traffic monitoring, surveillance, and security. DBM is extremely challenging in scenarios like illumination changes, camouflage, intermittent object motion or shadows. In this study, we proposed an end-to-end framework based on Generative Adversarial Network, which can generate dynamic background information for the task of DBM in an unsupervised manner. Our proposed model can handle the problem of DBM in the presence of the challenges mentioned above by generating data similar to the desired information. The primary aim of our proposed model during training is to learn all the dynamic changes in a scene-specific background information. While, during testing, inverse mapping of data to latent space representation in our model generates dynamic backgrounds similar to test data. The comparative analysis of our proposed model upon experimental evaluations on SBM.net and SBI benchmark datasets has outperformed eight existing methods for DBM in many challenging scenarios.