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[Lab image] People Image Analysis Consortium (PIA)
Head: Takeo Kanade
Contact: Jun-Su Jang (jsjang@cs.cmu.edu)

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

Associated center: VASC

For more information, see this lab's homepage.

Jump to: Lab Description | Personnel | Projects | Publications

Lab Description

The People Image Analysis (PIA) Consortium develops and distributes technologies that process images and videos to detect, track, and understand peoples' faces, bodies, and activities. The areas of technology that the PIA Consortium focus on include detection and tracking of humans, face recognition, facial expression analysis, gait analysis, and activity recognition. The goal of the Consortium is to develop a comprehensive set of imaging and processing tools, systems, and subsystems that work in the real-world environment.

Please see our official homepage for further information.

Personnel [Past members]

Name Title Email Address
Ankur's personal homepage Ankur Datta PhD Student, RI adatta@andrew.cmu.edu
Lie's personal homepage Lie Gu PhD Student, CS lgu@andrew.cmu.edu
Jun-Su Jang Postdoctoral Fellow jsjang@cs.cmu.edu
Takeo's personal homepage Takeo Kanade U.A. and Helen Whitaker University Prof., RI/CS tk@cs.cmu.edu
Yan's personal homepage Yan Ke PhD Student, CS yke@cmu.edu
Yan Li PhD Student, ECE yanli@cs.cmu.edu
Yaser Ajmal Sheikh Postdoctoral Fellow yaser@andrew.cmu.edu

Current Projects [Past Projects]

3D Head Motion Recovery in Real Time - A cylindrical model-based algorithm recovers the full motion (3D rotations and 3D translations) of the head in real time.
AAM Fitting Algorithms - Many varieties of algorithms for fitting Cootes and Taylor's "Active Appearance Models" are developed.
Accurate Camera Calibration from Planar Patterns - A novel camera calibration method can increases not only an accuracy of intrinsic camera parameters but also an accuracy of stereo camera calibration by utilizing a single framework for square, circle, and ring planar calibration patterns.
Event Detection in Videos - Our event detection method can detect a wide range of actions in video by correlating spatio-temporal shapes to over-segmented videos without background subtraction.
Feature-based 3D Head Tracking - A feature-based head tracking algorithm can handle occlusions and fast motion of face.
Frontal Face Alignment - This face alignment method detects generic frontal faces with large appearance variations and 2D pose changes and identifies detailed facial structures in images.
Hand Tracking and 3-D Pose Estimation - A 2-D and 3-D model-based tracking method can track a human hand rapidly moving and deformed on complicated backgrounds and recover its 3-D pose parameters.
Multi-People Tracking - Our multi-people tracking method can automatically initialize and terminate paths of people and follow multiple and changeable number of people on cluttered scenes over long time intervals.
Multi-view Car Detection and Registration - This method can detect cars with occlusions and varying viewpoints from a single still images by using multi-class boosting algorithm.
Real-time Face Detection - A face detection system has an accurate detection rate and real time performance by using an ensemble of weak classifiers.
Spatio-Temporal Facial Expression Segmentation - A two-step approach temporally segment facial gestures from video sequences. It can register the rigid and non-rigid motion of the face.
 

Recent publications [View all 61 publications]

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