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.





Projects  (side projects?)

Autoregressive Pedestrian Detection

Pedestrian Detection Deep Learning Caffe

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.

Illuminating Pedestrians via Simultaneous Detection & Segmentation (ICCV 2017)

Pedestrian Detection Deep Learning Caffe

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.

Pedestrian Detection with 30 Class Semantic Segmentation (Video Demonstration)

Object Recognition Deep Learning Caffe

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.