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In this paper we propose an end-to-end swift 3D feature reductionistframework (3DFR) for scene independent change detection. The 3DFR frameworkconsists of three feature streams: a swift 3D feature reductionist stream(AvFeat), a contemporary feature stream (ConFeat) and a temporal median featuremap. These multilateral foreground/background features are further refinedthrough an encoder-decoder network. As a result, the proposed framework notonly 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 independentevaluation scheme in order to demonstrate the robustness and generalizationcapability of the network. The performance of the proposed method is evaluatedon the benchmark CDnet 2014 dataset. The experimental results show that theproposed 3DFR network outperforms the state-of-the-art approaches.

3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection