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Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks but these performance improvements only focus on a single contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN by splitting the model into components and dynamically distributing these components in the cloud for optimal performance for real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform encompasses cloud servers and edge embedded devices for real-time object detection on video streams.