3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection
This addresses the problem of robust change detection across different scenes for applications like surveillance or monitoring, though it appears incremental as it builds on existing feature-based approaches.
The paper tackles scene-independent change detection by proposing the 3DFR framework, which uses multiple feature streams and an encoder-decoder network to detect changes and learn appearance features, and it outperforms state-of-the-art methods on the CDnet 2014 dataset.
In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection. The 3DFR framework consists of three feature streams: a swift 3D feature reductionist stream (AvFeat), a contemporary feature stream (ConFeat) and a temporal median feature map. These multilateral foreground/background features are further refined through an encoder-decoder network. As a result, the proposed framework not only detects temporal changes but also learns high-level appearance features. Thus, it incorporates the object semantics for effective change detection. Furthermore, the proposed framework is validated through a scene independent evaluation scheme in order to demonstrate the robustness and generalization capability of the network. The performance of the proposed method is evaluated on the benchmark CDnet 2014 dataset. The experimental results show that the proposed 3DFR network outperforms the state-of-the-art approaches.