Songlin Piao was born on 8th July 1985. He received his bachelor degree in the area of computer science and technology from Shanghai Jiao Tong University in Feburary 2009. From October 2005 to December 2006 he studied in Saarland University in Germany as an exchange student. He has also received a master's degree from Hanyang University South Korea in the department of electrical and computer engineering in February 2011. Songlin was working in the image engineering lab during his master period. The research interests includes computer vision, software engineering, object tracking especially using particle filters. He is now involved in the projects, which focus on solving safety issue around commercial vehicles using computer vision algorithms. He is responsible for Autonomous Low Speed Tractor and AsiMAT projects.
- Research on eclipse based media art authoring tool for the media artist.
Proceedings of the 9th international conference on Entertainment computing, S. 342 - 349. (2010)
- Multi-Object Tracking Based on Tracking-Learning-Detection Framework.
Field and Assistive Robotics - Advances in Systems and Algorithms, S. 74 - 87. (2014)
- Vision Based Person Detection for Safe Navigation of Commercial Vehicle.
Proceedings of the 13the International Conference on Intelligent Autonomous Systems (IAS-13), (2014-July 15-19)
- Adaptive Particle Filter based on the Kurtosis of Distribution.
- Adaptive sampling based on the motion.
2010 International Conference on Modeling, Simulation and Control, S. 336 - 340. (2010)
- Virtual Development on Mixed Abstraction Levels. an Agricultural Vehicle Case Study.
Synopsys Users Group(SNUG) Germany 2015, (2015)
- Real-time multi-platform pedestrian detection in a heavy duty driver assistance system.
Commercial Vehicle Technology 2016 – Proceedings of the Commercial Vehicle Technology Symposium (CVT 2016), S. 61 - 70. (2016-March 8-10)
- Safeguarding of Commercial Vehicle for Autonomous Mode.
Commercial Vehicle Technology 2016 – Proceedings of the Commercial Vehicle Technology Symposium (CVT 2016), (2016)
- Compact Data Association in Multiple Object Tracking. Pedestrian Tracking on Mobile Vehicle as Case Study.
9th IFAC Symposium on Intelligent Autonomous Vehicles, (2016)