I am a 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, semantic forecasting, and efficient binary-weight CNNs among others.
We propose an autoregressive cascaded phase network designed to progressively improve precision. The proposed framework utilizes a lightweight and stackable de-encoder module with inner-lateral convolutions designed to encourage features to both refine and diversify. We explicitly encourage
increasing levels of precision by assigning strict labeling
policies to each consecutive phase such that early phases
develop features primarily focused on achieving high recall
and later on accurate precision. In consequence, the final
feature maps form more peaky radial gradients emulating
from the centroids of unique pedestrians.
Collectively, our framework leads new state-of-the-art
performance on challenging settings of Caltech.
Project and code will be released upon publication.
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.