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A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, pre-processed and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this work is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematicaldoi:10.1109/tsmc.2018.2840341 fatcat:y6fp7xy6ufen5pae4qjctuc6au