I am a second year PhD student at Michigan State University in the department of Computer Science and Engineering working in the Computer Vision Lab advised by Dr. Xiaoming Liu.
My primary interests are in deep learning, computer vision, autonomous driving. I focus on object recognition in the realm urban scenes. This involves work in pedestrian detection, object detection, instance semantic segmenation, among others.
In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection.
Find out more at project website, publication, and source code.
Monocular urban pedestrian detection system featuring a cascade of networks, and a 30 class semantic segmentation side task jointly trained using Caltech and Cityscapes.
The classes utilized for segmentation are based on classes commonly seen in urban street scenes including roads, buildings, vehicles, pedestrians, traffic signs, and trees. Video is captured on Michigan State University campus in conjunction with the the CANVAS autonomous driving effort.