Research

Research Topics

Event-based Vision

Supervised learning and low-power processing of data from event based cameras such as dynamic vision sensor (DVS), Asynchronous Time-based Image Sensor (ATIS) etc., is another major focus of research in our team. We are looking to use reconfigurable hardware, such as FPGAs, and ARM processors, for implementation of novel event-based algorithms for power efficient computing.

The Xilinx Zynq-7020 FPGA was interfaced to a down-looking DAVIS camera, on-board an unmanned aerial vehicle, recognizing different objects beneath it.

Besides low-power hardware implementations, we also carry out academically oriented work for object tracking, learning and detection.

Spiking Neural Networks

Spiking Neural Networks use biologically plausible models of neuron as their computational unit. It is one of the major foucus of our research, especially supervised learning for processing data from event based senosrs such as silicon retina, silicon cochlea etc. We are also looking to configure spiking neural networks for implementation in neuromorphic hardwares for power efficient computing.

Publications

Journals

Bharath Ramesh*, Andrés Ussa, Luca Della Vedova, Hong Yang and Garrick Orchard., "Low-power Dynamic Object Detection and Classification with Freely Moving Event Cameras" Frontiers in Neuroscience (Accepted), Feb. 2020 link

Ramesh B., Yang H., Orchard G., Le Thi N., Zhang S. and Xiang C. "DART: Distribution Aware Retinal Transform for Event-based Cameras" IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2019.2919301 , May 2019 link

Zhen X., Sheng Y.C. and Orchard, G. "Event-based Stereo Depth Estimation Using Belief Propagation" Frontiers in Neuroscience vol. 11, pp. 535, Oct. 2017 link

Conference proceedings

Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu, Andres Ussa, Bharath Ramesh, and Arindam Basu, "HyNNA: Improved Performance for Neuromorphic Vision Sensor Based Surveillance Using Hybrid Neural Network Architecture", IEEE International Symposium on Circuits and Systems (ISCAS) , Sevilla, Spain, May 2020 (Accepted)

Andres Ussa, Luca Della Vedova, Vandana Reddy Padala, Deepak Singla, Jyotibdha Acharya, Charles Zhang Lei, Garrick Orchard, Arindam Basu, Bharath Ramesh., "A low-power end-to-end hybrid neuromorphic framework for surveillance applications", British Machine Vision Conference Workshops, Cardiff, United Kingdom, Sep 2019 PDF, video

Jyotibdha Acharya, Andres Ussa Caycedo, Vandana Reddy Padala, Rishi Raj Sidhu Singh, Garrick Orchard, Bharath Ramesh, Arindam Basu., "EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors", IEEE International System-on-Chip Conference (SOCC), Singapore, Sep 2019 PDF, video

Shrestha S.B. and Orchard G. "SLAYER: Spike Layer Error Reassignment in Time", Neural Information Processing Systems , Montreal, Canada, Dec 2018 PDF, video

Ramesh B., Ussa Caycedo A. C., Della Vedova L., Yang H. and Orchard G. "PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras", Asian Conference on Computer Vision, Perth, Australia, Dec 2018

Colonnier F., Della Vedova L., Teo R.S.H and Orchard G. "Obstacle Avoidance using Eventbased Visual Sensor and Time-To-Contact Processing", Australasian Conference on Robotics and Automation, Lincoln, New Zealand, Dec 2018 PDF, video

Ramesh B., Zhang S., Lee Z.W., Gao Z., Orchard G. and Xiang C. "Long-term Object Tracking with a Moving Event Camera", British Machine Vision Conference, Newcastle, England, Sept 2018 PDF, video

Tun Aung M., Teo R. and Orchard G. “Event-based Plane-fitting Optical Flow for Dynamic Vision Sensors in FPGA” IEEE Int. Symp. Circuits Syst., Florence, Italy, May 2018

Ramesh B., Le Thi N., Orchard G. and Xiang C. "Spike Context: A Neuromorphic Descriptor for Pattern Recognition", IEEE Biomed. Circuits Syst., Turin, Italy, Oct 2017

Czech D. and Orchard G. "Evaluating Noise Filtering for Event-based Asynchronous Change Detection Image Sensors" 6th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Singapore, June 2016

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