JIGSAW: Edge-based streaming perception over spatially overlapped multi-camera deployments

• Main Authors: GOKARN, Ila, HU, Yigong, ABDELZAHER, Tarek, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Edge AI; Machine Perception; Canvas-based Processing; Artificial Intelligence and Robotics; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9222; https://ink.library.smu.edu.sg/context/sis_research/article/10226/viewcontent/ICME2024_JigSaw_Cameraready.pdf
• Institution: Singapore Management University

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 cameras on a single Jetson TX2 with a 66.6% increase in accuracy and a simultaneous 18x (1800%) gain in cumulative throughput (475 FPS), far outperforming competitive baselines.