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Moving object segmentation in video plays an important role in many computer vision applications such as outdoor video surveillance, robotics, self-driving cars, etc. Existing work comprises complicated training procedures considering more number of history frames, fine-tuning on test sequences, and pre-trained models adopted from other applications. Therefore, this paper proposes an end-to-end robust inter-frame change exposure (RIChEx) framework for segmenting moving objects using three consecutive frames. The RIChEx consists inter-frame change gradient (IFCG) aggregation approach responsible for extracting the relevant foreground features to capture substantial changes and spatio-temporal structural dependencies between the consecutive frames. This module consists of multi-scale addition (MSA) blocks for extracting multi-scale features in early layers. Furthermore, to enhance the temporal learning capability of the module, previous recurrent responses and optical flow information are integrated to estimate probable foreground availability and salient object motions. We have evaluated the proposed framework on the benchmark DAVIS-16 dataset to validate the model's performance.