Balancing Latency and Accuracy on Deep Video Analytics at the Edge

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

70 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
Otsikko2022 IEEE 19th Annual Consumer Communications & Networking Conference
KustantajaIEEE
Sivut299-306
Sivumäärä8
ISBN (elektroninen)978-1-6654-3161-3
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Consumer Communications and Networking Conference - Las Vegas, Yhdysvallat
Kesto: 8 tammik. 202211 tammik. 2022
Konferenssinumero: 19

Conference

ConferenceIEEE Consumer Communications and Networking Conference
LyhennettäCCNC
Maa/AlueYhdysvallat
KaupunkiLas Vegas
Ajanjakso08/01/202211/01/2022

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