@inproceedings{41be396a426d4f1e9e66f51f456eef40,
title = "Data-driven windows to accelerate video stream content extraction in complex event processing",
abstract = "This work presents a data-driven adaptive windowing approach to accelerate video content extraction in DNN-based Complex Event Processing (CEP) systems. The CEP windows continuously monitor low-level content of incoming video frames and exploit interframe correlations to accelerate the overall DNN content extraction process. The two main contributions are: 1) technique to create micro-batches of similar frames within the window by measuring dissimilarities among them, and 2) optimal frame resolution within micro-batches under specified accuracy thresholds for fast model processing. The initial experimental results show that our adaptive micro-batching approach improves 3.75X model throughput execution while maintaining application-level latency bounds under required accuracy constraints.",
keywords = "Complex Event Processing, Deep Neural Network, High Throughput, Video Processing, Windows",
author = "Piyush Yadav and Das, \{Dibya Prakash\} and Edward Curry",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s).; 20th International Middleware Conference Demos and Posters, Middleware 2019 ; Conference date: 09-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
day = "9",
doi = "10.1145/3366627.3368115",
language = "English",
series = "Middleware Demos and Posters 2019 - Proceedings of the 2019 20th International Middleware Conference Demos and Posters, Part of Middleware 2019",
publisher = "Association for Computing Machinery, Inc",
pages = "15--16",
booktitle = "Middleware Demos and Posters 2019 - Proceedings of the 2019 20th International Middleware Conference Demos and Posters, Part of Middleware 2019",
}