11 citations · 27 across the 12 of their papers we have counts for
12 papers
Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Piotr Wzorek, Kamil Jeziorek, Tomasz Kryjak
Event-based cameras are gaining popularity as the sensor of choice for mobile robotics, due to their high performance in dynamic environments. However, these applications require e…
Event Detection in Videos: A Framework for the Development of New Methods
Anastasia Zakharova, Thierry Bouwmans, Anthony Cioppa +13
Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods inte…
End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor
Wiktor Matykiewicz, Piotr Wzorek, Kamil Jeziorek +4
With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on-device data processing on edge platforms. A promising research di…
Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +6
As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-awa…
Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection
Marcin Kowalczyk, Kamil Jeziorek, Tomasz Kryjak
Event-based sensors offer significant advantages over traditional frame-based cameras, especially in scenarios involving rapid motion or challenging lighting conditions. However, e…
Self-Supervised Event Representations: Towards Accurate, Real-Time Perception on SoC FPGAs
Kamil Jeziorek, Tomasz Kryjak
Event cameras offer significant advantages over traditional frame-based sensors. These include microsecond temporal resolution, robustness under varying lighting conditions and low…
Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
Hiroshi Nakano, Krzysztof Blachut, Kamil Jeziorek +6
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, b…
Increasing the scalability of graph convolution for FPGA-implemented event-based vision
Piotr Wzorek, Kamil Jeziorek, Tomasz Kryjak +1
Event cameras are becoming increasingly popular as an alternative to traditional frame-based vision sensors, especially in mobile robotics. Taking full advantage of their high temp…
Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +2
The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems. Particularly within the automo…
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +2
Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are…
Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras
Kamil Jeziorek, Andrea Pinna, Tomasz Kryjak
Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high d…
Traffic sign detection and recognition using event camera image reconstruction
Kamil Jeziorek, Tomasz Kryjak
This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neur…