The goal of the PILab is to gain useful technologies on perception and intelligence which can be applied to visual surveillance systems, inspection machines, robots, etc. We are developing perceptual primitives to detect, track, and recognize human/vehicles, faces and to understand abnormal behaviors and situations through visual information. In addition, we are developing incremental learning models including probabilistic learning machines for integrating multiple sensory modalities in the changing environments.
Hyung Jin Chang, Hawook Jeong, and Jin Young Choi, "Active Attentional Sampling for Speed-up of Background Subtraction" IEEE Proc. Computer Vision and Pattern Recognition (CVPR), 2012.
JinMin Choi, Hyung Jin Chang, Yung Jun Yoo, Jin Young Choi, "Robust Moving Object Detection against Fast Illumination Change," Computer Vision and Image Understanding (CVIU), pages 179-193, Vol.116, Issue 2, February 2012.
Hyung Jin Chang, Dong Sung Song, Pyo Jae Kim, and Jin Young Choi, "Spatio-temporal Pattern Modeling for Fault Detection and Classification in Semiconductor Manufacturing," IEEE Transanctions on Semiconductor Manufacturing, Vol. 25, No. 1, Feb 2012.
Hyung Jin Chang, Kwang Moo Yi, Shimin Yin, Soo Wan Kim, Young Min Baek, Ho Seok Ahn, and Jin Young Choi, "PIL-EYE: Integrated System for Sustainable Development of Intelligent Visual Surveillance Algorithms," IEEE Digital Image Computing: Techniques and Applications (DICTA) 2011, December 6-8, 2011.
Kimin Yun, Soo Wan Kim, Jin Young Choi, "Probabilistic Approach with Three Hierarchies of Motion Esimation for Video Stabilization," IEEE Digital Image Computing: Techniques and Applications 2011, December, 2011.
Hyung Jin Chang, Myoung Soo Park, Hawook Jeong, and Jin Young Choi, "Tracking Failure Detection by Imitating Human Visual Perception," IEEE International Conference on Image Processing (ICIP) 2011, September, 2011.
Hawook Jeong, Hyung Jin Chang, and Jin Young Choi, "Modeling of Moving Object Trajectory by Spatio-temporal Learning for Abnormal Behavior Detection," 2011 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), August, 2011.