Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras

TL;DR

This paper presents a comprehensive system for customized deep learning video analytics on surveillance cameras, demonstrating superior accuracy, resource-efficient deployment, and real-world application across security and safety scenarios.

Contribution

It introduces a complete pipeline for training and deploying custom deep learning models on embedded devices for surveillance, with extensive real-world evaluations.

Findings

Custom models outperform off-the-shelf models in accuracy.

Resource-constrained models can be deployed on embedded devices.

First comprehensive evaluation of deep learning models in real-world surveillance scenarios.

Abstract

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.