Publishing at the Irish Machine Vision and Image Processing Conference

The IMVIP Conference is Ireland’s primary meeting for those researching in the fields of machine vision and image processing. The conference has been running since 1997 and provides a forum for the exchange of ideas and the presentation of research conducted both in Ireland and worldwide.

The D²iCE group presented two papers in the 2022 iteration of this conference.

Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation

Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS-COCO, a non-automotive dataset, contains human pose estimation. In this work, we propose a multi-task framework to extract pedestrian features through detection and instance segmentation tasks performed separately on these two distributions. Thereafter, an encoder learns pose specific features using an unsupervised instance-level domain adaptation method for the pedestrian instances from both distributions. The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.

Classification of electromagnetic interference induced image noise in an analog video link

With the ever-increasing electrification of the vehicle showing no sign of retreating, electronic systems deployed in automotive applications are subject to more stringent Electromagnetic Immunity compliance constraints than ever before, to ensure the proximity of nearby electronic systems will not affect their operation. The EMI compliance testing of an analog camera link requires video quality to be monitored and assessed to validate such compliance, which up to now, has been a manual task. Due to the nature of human interpretation, this is open to inconsistency. Here, we propose a solution using deep learning models that analyse, and grade video content derived from an EMI compliance test. These models are trained using a dataset built entirely from real test image data to ensure the accuracy of the resultant model(s) is maximised. Starting with the standard AlexNet, we propose four models to classify the EMI noise level.

The full proceedings for IMVIP 2022 can be found here.