Title: NoScope: Optimizing Neural Network Queries over Video at Scale,
Abstract: Recent advances in computer vision-in the form of deep neural networks-have
made it possible to query increasing volumes of video data with high accuracy.
However, neural network inference is computationally expensive at scale:
applying a state-of-the-art object detector in real time (i.e., 30+ frames per
second) to a single video requires a $4000 GPU. In response, we present
NoScope, a system for querying videos that can reduce the cost of neural
network video analysis by up to three orders of magnitude via
inference-optimized model search. Given a target video, object to detect, and
reference neural network, NoScope automatically searches for and trains a
sequence, or cascade, of models that preserves the accuracy of the reference
network but is specialized to the target video and are therefore far less
computationally expensive. NoScope cascades two types of models: specialized
models that forego the full generality of the reference model but faithfully
mimic its behavior for the target video and object; and difference detectors
that highlight temporal differences across frames. We show that the optimal
cascade architecture differs across videos and objects, so NoScope uses an
efficient cost-based optimizer to search across models and cascades. With this
approach, NoScope achieves two to three order of magnitude speed-ups
(265-15,500x real-time) on binary classification tasks over fixed-angle webcam
and surveillance video while maintaining accuracy within 1-5% of
state-of-the-art neural networks.