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This paper demonstrates the effectiveness of our customized deep learningbased video analytics system in various applications focused on security,safety, customer analytics and process compliance. We describe our videoanalytics system comprising of Search, Summarize, Statistics and real-timealerting, and outline its building blocks. These building blocks include objectdetection, tracking, face detection and recognition, human and facesub-attribute analytics. In each case, we demonstrate how custom models trainedusing data from the deployment scenarios provide considerably superioraccuracies than off-the-shelf models. Towards this end, we describe our dataprocessing and model training pipeline, which can train and fine-tune modelsfrom videos with a quick turnaround time. Finally, since most of these modelsare deployed on-site, it is important to have resource constrained models whichdo not require GPUs. We demonstrate how we custom train resource constrainedmodels and deploy them on embedded devices without significant loss inaccuracy. To our knowledge, this is the first work which provides acomprehensive evaluation of different deep learning models on variousreal-world customer deployment scenarios of surveillance video analytics. Bysharing our implementation details and the experiences learned from deployingcustomized deep learning models for various customers, we hope that customizeddeep learning based video analytics is widely incorporated in commercialproducts around the world.

Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras