TY - GEN
T1 - Demo abstract
T2 - 16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018
AU - Zhao, Zhihe
AU - Jiang, Zhehao
AU - Ling, Neiwen
AU - Shuai, Xian
AU - Xing, Guoliang
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11/4
Y1 - 2018/11/4
N2 - Real-time image-based object tracking from live video is of great importance for several smart city applications like surveillance, intelligent traffic management and autonomous driving. Although recent deep learning systems can achieve satisfactory tracking performance, they incur significant compute overhead, which prevents them from wide adoption on resource-constrained IoT platforms. In this demonstration, we present an Edge Computing system for Real-time object Tracking (ECRT) for resource-constrained devices. The key feature of our system is that it intelligently partitions compute-intensive tasks such as inferencing a convolutional neural network(CNN) into two parts, which are executed locally on an IoT device and/or on the edge server. Moreover, ECRT can minimize the power consumption of IoT devices while taking into consideration the dynamic network environment and user requirement on end to end delay.
AB - Real-time image-based object tracking from live video is of great importance for several smart city applications like surveillance, intelligent traffic management and autonomous driving. Although recent deep learning systems can achieve satisfactory tracking performance, they incur significant compute overhead, which prevents them from wide adoption on resource-constrained IoT platforms. In this demonstration, we present an Edge Computing system for Real-time object Tracking (ECRT) for resource-constrained devices. The key feature of our system is that it intelligently partitions compute-intensive tasks such as inferencing a convolutional neural network(CNN) into two parts, which are executed locally on an IoT device and/or on the edge server. Moreover, ECRT can minimize the power consumption of IoT devices while taking into consideration the dynamic network environment and user requirement on end to end delay.
KW - Computer vision
KW - Edge computing
KW - Real-time embedded system
UR - http://www.scopus.com/inward/record.url?scp=85061501195&partnerID=8YFLogxK
U2 - 10.1145/3274783.3275199
DO - 10.1145/3274783.3275199
M3 - Conference Proceeding
AN - SCOPUS:85061501195
T3 - SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems
SP - 394
EP - 395
BT - SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 4 November 2018 through 7 November 2018
ER -
