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Yipu Zhao

Key Lab of Machine Perception (MOE)

School of Electronics Engineering and Computer Science

Peking University

Email 1: zhaoyp /AT/ cis.pku.edu.cn

Email 2: zhaoyipu /AT/ gmail.com

 Wordle: test

Education
I received my BSE from Department of EECS, Peking University in July 2010. Currently I am a master student at the Lab of 3D Vision Computing and Robotics, Peking University, advised by Prof. Huijing Zhao and Prof. Hongbin Zha.

Research

1. Trajectory Processing

For applications like trajectory analysis, scene modeling and abnormal detection, trajectories of good quality are required. However, for current moving object tracking systems, split/merge events happen frequently and reduce trajectory quality greatly. Therefore, a trajectory processing method that improves the quality of tracking results is necessary. A demo of our approach could be downloaded here(trajectory processing.avi)

 

2. 3D Object Extraction

An object extraction system is developed on our vehicle-borne sensing platform POSS-v. Unlike most similar approaches that follow a bottom-up procedure (Segmentation, Labeling & Merging), our approach focus on a subset of sensing data labeled as "salient". The salient data belong to some pre-defined classes of objects, and are highlighted by a graph matching procedure.

 

3. Lane Detection & Tracking

Demands on real time lane detection & tracking are great for robotics, ADAS and autonomous vehicles. A lane detecion & tracking system is developed and tested on our vehicle platform POSS-v, also a cross-line alerting module is introduced. Some demo videos can be downloaded here(lane detection, lane tracking.avi).

We also develop a web-based interface for manualy labeling traffic lanes from video data, using the Bing Mapping SDK. A demo page can be visited, and some sample data can be found at 40.0364N,116.306E.

In addition, some demos of POI extraction interface are provided(line label.avi, plane label.avi).

 

Publications
  1. Computing Object-based Saliency in Urban Scenes Using Laser Sensing,
    Y. Zhao, M. He, H. Zhao, F. Davoine, H. Zha,
    IEEE Int. Conf. on Robotics and Automation (ICRA), 2012.
    paper    , ppt
  2. Moving Object Trajectory Processing Based on Multi-Laser Sensing,
    Y. Zhao, H. Zhao, J. Sha, H. Zha,
    IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), 2011.
    paper    , ppt, video
  3. Trajectory Analysis of Moviing Objects at Intersection Based on Laser-Data,
    J. Sha, Y. Zhao, W. Xu, H. Zhao, J. Cui, H. Zha,
    IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), 2011.
    paper    , ppt
  4. Moving Object Detection and Tracking at Intersections using a Network of Laser Scanners,
    H. Zhao, J. Sha, Y. Zhao, J. Cui, H. Zha, R. Shibasaki,
    IEEE Tran. on Intelligent Transportation Systems, 2012.
    paper

Datasets

1. Urban Laser Sensing Dataset

The dataset is collect by a SICK LMS-200 LiDAR mounted on the rightside of the vehicle's roof, with the ground data removed already. It contains a variety of objects appearing frequently in urban scenes. Data files are provided in ascii format: x y z label, one point per line, space as separator. We manually labeled 16 classes of objects (the left parts are undefine), which are listed below. Download here.

Class
Label
Undefine
0
Car
1
Bus
2
Truck
3
Bicycle
4
Pedestrian
5
Pole
6
Traffic light
7
Road lamp
8
Road sign
9
Traffic sign
10
Trash box
11
Curb
12
Pile
13
Building
14
Fence
15
Adboard
16

Technical Skills
  • Languages: C/C++; C#; Matlab; Python;
  • Tools: Qt; MFC; OpenGL; OpenCV; PCL; MRPT;
  • Internet Technology: HTML/CSS; PHP;
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