Bharath’s main research interests include pattern recognition and computer vision. At present, his research is centered on event-based cameras for autonomous sensing and navigation. This includes tracking and recognition with an array of sensors, most importantly event-based cameras, to be processed efficiently on low-power devices to yield accurate results at real-time. In the past, he has mostly worked on object recognition and related areas such as scene understanding, face recognition, and object detection. He received the B.E. degree in electrical & electronics engineering from Anna University of India in 2009; M.Sc. and Ph.D. degrees in electrical engineering from National University of Singapore in 2011 and 2015 respectively, working at the Control and Simulation Laboratory on Image Classification using Invariant Features.
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace. However, inference that needs to largely take place on the ‘edge’ (not processed on servers), is a highly computational and memory intensive workload, making it intractable for low-power nodes/applications like mobile and remote devices. This talk presents an end-to-end framework for object tracking and classification that uses neuromorphic dynamic vision sensors (DVS) that possess desirable properties such as low power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-based processing with DVS, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region-proposal method based on the density of foreground events, object tracking is implemented using the apparent object velocity while tackling occlusion scenarios. For low-power classification of the tracked objects, the DVS is interfaced to IBM Truenorth and time-multiplexed to tackle up to eight instances simultaneously for traffic monitoring using energy efficient deep network (EEDN) pipeline. Finally, offline comparisons to state-of-the-art event-based systems for object tracking and classification strengthens the use case of our neuromorphic framework for low-power applications.
Time: 23 May, Thursday, 2019, 10:00-11:00AM
Host: Dr. Shiyu Zhao, School of Engineering
Venue: Meeting Room 203, 2nd Floor, Build 5, Yunqi Campus