Hello World! 😎
I am Chenge Li, a fifth year PhD student studying machine learning and computer vision at the Video Lab at New York University. My supervisor is Prof. Yao Wang. Prior to NYU, I have obtained Bachelor's degree in Communication Engineering from Tianjin University, China. I have also spent a wonderful junior year exchanging at The University of Hong Kong.
I am looking for a full-time position after my expected graduation in December 2018. 🎓
Object detection and object tracking are usually treated as two separate processes. Object detection in still images relies on spatial appearance features, whereas object tracking in videos relies on both spatial appearance and temporal motion features. Significant progress has been made for object detection in 2D images (or video frames) using deep learning networks such as region CNN and subsequent variants. The usual pipeline for object tracking requires that the object be successfully detected in the first frame or in every frame, and tracking is done by “associating” detection results. However, performing object detection and object tracking through a single network remains a challenging open question.
We propose a novel network structure that can directly detect a 3D tube enclosing a moving object in a video by extending the region-CNN framework for object detection in an image. The proposed trackNet works over short video segments and outputs a bounding tube for each detected moving object, which includes shifted bounding boxes covering the detected object in successive frames. A Tube Proposal Network (TPN) inside the trackNet is proposed to predict the objectiveness of each candidate tube and location parameters specifying the bounding tube with a high objectiveness score.
reference: All images displayed below are from ICME 2018 Grand Challenge Salient360! 2018: Visual attention modeling for 360 Images https://salient360.ls2n.fr/
Saliency detection qualitative result from our model:
A Comparison of Machine Learning and Feature Selection Methods for Predicting Breast Cancer Related Lymphedema [Master's Thesis]
Apple Inc. Cupertino, CA
Jun 2017 – Aug 2017 summer internship Computer vision research intern. Single image super resolution using convolutional neural networks.
-
Mei R. Fu, Yao Wang, Chenge Li, Zeyuan Qiu and Deborah Axelrod and Amber A. Guth and Joan Scagliola and Yvette Conley and Bradley E. Aouizerat and Jeanna M. Qiu and Gary Yu and Janet H. Van Cleave and Judith Haber and Ying Kuen Cheung. "Machine learning for detection of lymphedema among breast cancer survivors", mHealth, volume 4, 2018.
-
Chenge Li, Gregory Dobler, Yilin Song, Xin Feng, and Yao Wang. “TrackNet: Simultaneous Detection and Tracking of Multiple Objects” (under review)
-
Yilin Song, Chenge Li, and Yao Wang. “Pixel-wise object tracking” (under review)
-
Chenge Li, A. Chiang, Gregory Dobler, Y. Wang, Kun Xie, Kaan Ozbay, Masoud Ghandehari, J. Zhou, and D. Wang. "Robust vehicle tracking for urban traffic videos at intersections." In Advanced Video and Signal Based Surveillance (AVSS), 2016 13th IEEE International Conference on, pp. 207-213. IEEE, 2016.
-
Kun Xie, Chenge Li, Kaan Ozbay, Gregory Dobler, Hong Yang, An-Ti Chiang, and Masoud Ghandehari. "Development of a comprehensive framework for video-based safety assessment." In Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on, pp. 2638-2643. IEEE, 2016.
-
Yilin Song, Yuanyi Xue, Chenge Li, Xuan Zhao, Sixuan Liu, Xiaona Zhuo, Kangjin Zhang et al. "Online Cost Efficient Customer Recognition System for Retail Analytics." In Applications of Computer Vision Workshops (WACVW), 2017 IEEE Winter, pp. 9-16. IEEE, 2017.
-
Yuanyi Xue, Yilin Song, Chenge Li, An-Ti Chiang, and Xiaoran Ning. "Automatic Video Annotation System for Archival Sports Video." In Applications of Computer Vision Workshops (WACVW), 2017 IEEE Winter, pp. 23-28. IEEE, 2017.
Grand Prize for the MLBAM Automatic Video Annotation Challenge held by NYC Media Lab May 11, 2015
Last Update: June 8, 2018