Name
Abstract | ||
|---|---|---|
ABSTRACTMany private and public organizations deploy large numbers of cameras, which are used in application services for public safety, healthcare, and traffic control. Recent advances in deep learning have demonstrated remarkable accuracy on computer analytics tasks that are fundamental for these applications, such as object detection and action recognition. While deep learning opens the door for the automation of camera-based applications, deploying pipelines for live video analytics is still a complicated process that requires domain expertise in the fields of machine learning, computer vision, computer systems, and networks. The problem is further amplified when multiple pipelines need to be deployed on the same infrastructure to meet different users' diverse and yet dynamic needs. In this paper, we present a live-video-analytics-as-a-service vision, aiming to remove the complexity barrier and achieve flexibility, agility, and efficiency for applications based on live video analytics. We motivate our vision by identifying its requirements and the shortcomings of existing approaches. Based on our analysis, we present our envisioned system design and discuss the challenges that need to be addressed to make it a reality. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3517207.3526973 | European Conference on Computer Systems |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilherme H. Apostolo | 1 | 0 | 0.34 |
| Pablo Bauszat | 2 | 0 | 0.34 |
| Vinod Nigade | 3 | 16 | 1.01 |
| Henri E. Bal | 4 | 3214 | 301.96 |
| Lin Wang | 5 | 0 | 1.01 |