Poster abstract: 3D activity localization with multiple sensors
Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017
Activity recognition; Activity tracking; Deep learning; Locolization; Passive RFID
© 2017 ACM. We present a deep learning framework for fast 3D activity localization and tracking in a dynamic and crowded real world seting. Our training approach reverses the traditional activity localization approach, which first estimates the possible location of activities and then predicts their occurrence. Instead, we first trained a deep convolutional neural network for activity recognition using depth video and RFID data as input, and then used the activation maps of the network to locate the recognized activity in the 3D space. Our system achieved around 20cm average localization error (in a 4m × 5m room) which is comparable to Kinect's body skeleton tracking error (10-20cm), but our system tracks activities instead of Kinect's location of people.
Li, X., Zhang, Y., Zhang, J., Chen, S., Gu, Y., Farneth, R., Marsic, I., & Burd, R. (2017). Poster abstract: 3D activity localization with multiple sensors. Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017, (). http://dx.doi.org/10.1145/3055031.3055057