Balancing Latency and Accuracy on Deep Video Analytics at the Edge

Xuebing Li, Byung Cho, Yu Xiao

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)
169 Lataukset (Pure)


Real-time deep video analytic at the edge is an enabling technology for emerging applications, such as vulnerable road user detection for autonomous driving, which requires highly accurate results of model inference within a low latency. In this paper, we investigate the accuracy-latency trade-off in the design and implementation of real-time deep video analytic at the edge. Without loss of generality, we select the widely used YOLO-based object detection and WebRTC-based video streaming for case study. Here, the latency consists of both networking latency caused by video streaming and the processing latency for video encoding/decoding and model inference. We conduct extensive measurements to figure out how the dynamically changing settings of video streaming affect the achieved latency, the quality of video, and further the accuracy of model inference. Based on the findings, we propose a mechanism for adapting video streaming settings (i.e. bitrate, resolution) online to optimize the accuracy of video analytic within latency constraints. The mechanism has proved, through a simulated setup, to be efficient in searching the optimal settings.
Otsikko2022 IEEE 19th Annual Consumer Communications & Networking Conference
ISBN (elektroninen)978-1-6654-3161-3
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Consumer Communications and Networking Conference - Las Vegas, Yhdysvallat
Kesto: 8 tammik. 202211 tammik. 2022
Konferenssinumero: 19


NimiIEEE Consumer Communications and Networking Conference
ISSN (elektroninen)2331-9860


ConferenceIEEE Consumer Communications and Networking Conference
KaupunkiLas Vegas


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