Recent research shows that the multi-view system for object recognition outperforms the single-view point system. When viewpoints are added, additional communication cost and cost to deploy the viewpoints are also added. However, prior work has shown that not all of the views are useful, and poor viewpoints can be excluded. This paper explores the dynamic context application for a Context-Aware Neural Network. The Context-Aware Neural Network uses Shannon entropy value to acquire likelihood, and this likelihood value to reduce viewpoints in a distributed system. However, reducing viewpoints were done on static image recognition, so the spatial relation between the views and subject is fixed. Expansion to dynamic context is essential since most of the real world is a series of images, rather than a snapshot of the scene. Apart from testing on images of 3D CAD data, this paper illustrates the generation of 3D CAD data videos, and examines the video analysis of the generated videos using the Context-Aware Neural Network. In this particular setup, relevant objects move with respect to a fixed set of cameras. It is reported that the viewpoints can be reduced, and context of the trained data matters in the setup.