Search Results for author:
Found 5 papers, 2 papers with code
E-Scooter Rider Detection and Classification in Dense Urban Environments
Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic.
An Objective Method for Pedestrian Occlusion Level Classification
Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles.
The Impact of Partial Occlusion on Pedestrian Detectability
RetinaNet has the lowest overall detection performance across the range of occlusion levels.
Characterisation of CMOS Image Sensor Performance in Low Light Automotive Applications
This paper outlines a systematic method for characterisation of state of the art image sensor performance in response to noise, so as to improve the design and performance of automotive cameras in low light scenarios.
Impact of Power Supply Noise on Image Sensor Performance in Automotive Applications
Research has been undertaken to develop a systematic method of characterising image sensor performance in response to electrical noise in order to improve the design and performance of automotive cameras in low light scenarios.
