13 citations · 25 across the 7 of their papers we have counts for
7 papers · 1 filter
Ultra-low-power Image Classification on Neuromorphic Hardware
Gregor Lenz, Garrick Orchard, Sadique Sheik
Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to…
e-TLD: Event-based Framework for Dynamic Object Tracking
Bharath Ramesh, Shihao Zhang, Hong Yang +4
This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tra…
EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
Jyotibdha Acharya, Andres Ussa Caycedo, Vandana Reddy Padala +4
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT)…
A low-power end-to-end hybrid neuromorphic framework for surveillance applications
Andres Ussa, Luca Della Vedova, Vandana Reddy Padala +6
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 ta…
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
Bharath Ramesh, Andres Ussa, Luca Della Vedova +2
We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choos…
Event-based Vision: A Survey
Guillermo Gallego, Tobi Delbruck, Garrick Orchard +8
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness ch…