A Low Computational Cost Visual Tracking Algorithm Designed for a Multiple Mode Brain-Machine-Interface
- Resource Type
- Conference
- Authors
- Wang, Xuecheng; Zhang, Milin; Peng, Liangrui; Richardson, Andrew G.; Lucas, Timothy H.; Van der Spiegel, Jan
- Source
- 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) Electron Devices Technology & Manufacturing Conference (EDTM), 2020 4th IEEE. :1-4 Apr, 2020
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Photonics and Electrooptics
Power, Energy and Industry Applications
Wireless communication
Visualization
Target tracking
Neuroscience
Animals
Graphics processing units
Detectors
Visual tracking
neuroscience
machine learning
- Language
Intelligent tracking of the behavior information of animal subject is important in multiple mode brain-machine-interface implementation in neuroscience research. This work proposes a low latency, low computation complexity image-based tracking system with a wireless neural interface. The proposed tracker integrated a light scale feature points recognition network and a prediction module for real-time location tracking. A detector is used to initialize the location of the target animal as well as to fix the error generated from the tracker. Experimental results shows a state-of-the-art processing rate of 117fps based on 2.4GHz, 20 cores CPU resources and a 166fps based on a GPU server resources.