Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras
It addresses the problem of efficient and accurate video analytics for security, safety, and commercial applications, but it is incremental as it builds on existing deep learning methods with customizations for specific deployment scenarios.
This paper tackles the challenge of deploying deep learning-based video analytics on surveillance cameras by developing a system with custom models for object detection, tracking, and face recognition, showing that these models provide considerably superior accuracies than off-the-shelf ones and can be deployed on resource-constrained embedded devices without significant loss in accuracy.
This paper demonstrates the effectiveness of our customized deep learning based video analytics system in various applications focused on security, safety, customer analytics and process compliance. We describe our video analytics system comprising of Search, Summarize, Statistics and real-time alerting, and outline its building blocks. These building blocks include object detection, tracking, face detection and recognition, human and face sub-attribute analytics. In each case, we demonstrate how custom models trained using data from the deployment scenarios provide considerably superior accuracies than off-the-shelf models. Towards this end, we describe our data processing and model training pipeline, which can train and fine-tune models from videos with a quick turnaround time. Finally, since most of these models are deployed on-site, it is important to have resource constrained models which do not require GPUs. We demonstrate how we custom train resource constrained models and deploy them on embedded devices without significant loss in accuracy. To our knowledge, this is the first work which provides a comprehensive evaluation of different deep learning models on various real-world customer deployment scenarios of surveillance video analytics. By sharing our implementation details and the experiences learned from deploying customized deep learning models for various customers, we hope that customized deep learning based video analytics is widely incorporated in commercial products around the world.