2 citations · 2 across the 4 of their papers we have counts for
6 papers
Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data
Henok Teklu, Mustafa Sakhai, Matej Mertik +1
Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN detectors are trained with sup…
Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation
Henok Teklu, Mustafa Sakhai, Maciej Wielgosz +1
Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging conditions including motion blur,…
InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making
Mustafa Sakhaia, Kaung Sithua, Min Khant Soe Okea +1
Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often struggle in high-dynamic- r…
DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset
Mustafa Sakhai, Kaung Sithu, Min Khant Soe Oke +1
Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames, offering low latency, high dynamic range, and motion robustness. DVS-P…
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors
Mustafa Sakhai, Szymon Mazurek, Jakub Caputa +2
This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant c…
Modern Cybersecurity Solution using Supervised Machine Learning
Mustafa Sakhai, Maciej Wielgosz
Cybersecurity is essential, and attacks are rapidly growing and getting more challenging to detect. The traditional Firewall and Intrusion Detection system, even though it is widel…