Khalid Elgazzar, Professor, Engineering and Applied Science, Ontario Tech University
Thursday, December 14, 2023, 10:00
Building 1, Level 3, Room 3119
In this talk, I will present an innovative framework we developed to address these limitations and accurately predict pedestrian crossing intentions. At the core of the framework, we implement an image enhancement pipeline to enable the detection and rectification of various defects that may arise during unfavorable weather conditions. Following this, we employ a transformer-based network with a self-attention mechanism to predict the crossing intentions of pedestrians. This pipeline enhances the model's robustness and accuracy in classification tasks. We assessed our framework using the famous JAAD dataset. Performance metrics indicate that our model achieves state-of-the-art results while ensuring significantly low inference times.