Paper Info
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.
Year
DOI
Venue
2019
10.1145/3366627.3368115
Proceedings of the 20th International Middleware Conference Demos and Posters
Keywords
Field
DocType
Complex Event Processing, Deep Neural Network, High Throughput, Video Processing, Windows
Content extraction,Data-driven,Computer science,Complex event processing,Real-time computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-7042-4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Piyush Yadav100.34
Dibya Prakash Das200.68
Edward Curry3104.95