Name
Abstract | ||
|---|---|---|
Applying deep neural networks (DNNs) to video analytics tasks has drawn attention from both academic and industry communities. However, due to the high computational complexity of DNN models and the explosion of video data, it is challenging to process massive concurrent video queries efficiently and effectively. In this paper, we propose a video analytics system named DVQShare to process DNN-based video queries in a batch mode. The key idea is sharing, including time sharing, spatial sharing, and logical sharing. In principle, sharing across queries can help us reduce the overall amount of frames to be analyzed, which can help us improve the overall performance and reduce the monetary cost. Two modules are designed to process video queries by exploiting the above three sharing opportunities. First, an analysis module is integrated to guide the generation of query processing plans. Within this module, temporal sharing is considered to reuse historical results produced by other queries to remove pending frames that have been analyzed, and spatial sharing is adopted to avoid redundant processing over overlapping video clips. Additionally, we utilize logical sharing to further improve system's overall performance by considering the logical relationship between queries. Second, a query processing engine is devised to execute the query pipeline generated by the analysis module and return the final results. In experiments, we implement a prototype of the DVQShare system based on MXNet, and results show that it can achieve up to 2X performance speedup. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCGrid51090.2021.00026 | 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) |
Keywords | DocType | ISBN |
DNN,video analysis,video query processing,sharing,logical sharing | Conference | 978-1-7281-9587-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Fu | 1 | 23 | 9.38 |
| Shanjiang Tang | 2 | 19 | 5.08 |
| Ce Yu | 3 | 7 | 1.52 |
| Yusen Li | 4 | 26 | 4.25 |
| Jizhou Sun | 5 | 11 | 3.25 |
| Yanjie Liu | 6 | 0 | 0.34 |