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 (265-15,500 × 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.
Original language | English (US) |
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Pages (from-to) | 1586-1597 |
Number of pages | 12 |
Journal | Proceedings of the VLDB Endowment |
Volume | 10 |
Issue number | 11 |
DOIs | |
State | Published - Aug 1 2017 |
Externally published | Yes |
Event | 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: Aug 28 2017 → Sep 1 2017 |
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- General Computer Science