In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this project, we develop a scalable and flexible architecture called SIAT on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed architecture acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm.
SIAT exploits state-of-the-art distributed technologies and is composed of layered architecture.Read More
An intelligent video surveillance which has the ability to process both real-time streaming videos and offline batch processing in a scalable manner.Read More
We develop application scenarios for both online and offline distributed video data processing services.Read More
SIAT Research Objectives & Progress
In this research project, we try to propose a distributed, layered, service-oriented, and lambda style†inspired reference architecture for large-scale IVA in the cloud. Similarly, some more, objectives are listed below.
High-level abstraction on generic big data technologies for IVA in the cloud
IVA algorithms & IVA Service as-a-Service
Complex Event Analysis in Multistream Video Envrinoment
Propose real-world domain specific services on top of the SIAT
- 20 TB
- 1000 GB
- 100 GH
- 500 GH