|YoungJoon Yoo, Kimin Yun, Sangdoo Yun, JongHee Hong, Hawook Jeong and Jin Young Choi, "Visual Path Prediction in Complex Scenes with Crowded Moving Objects", CVPR 2016|
|Jongwon Choi, Hyung Jin Chang, Jiyeoup Jeong, Yiannis Demiris, and Jin Young Choi, "Attention-Modulated Visual Tracker Inspired by Structuralism Cognitive Model", CVPR2016|
|Sangdoo Yun et al., "Voting-based 3D Object Cuboid Detection Robust to Partial Occlusion from RGB-D Images", IEEE Winter Conference on Applications of Computer Vision (WACV), 2016|
|Moonsub Byeon et al., "Efficient Spatio-Temporal Data Association Using Multidimensional Assignment in Multi-Camera Multi-Target Tracking", British Machine Vision Conference (BMVC), 2015.|
|"Robust and Fast Moving Object Detection in a Non-Stationary Camera via Foreground Probability based Sampling", IEEE International Conference on Image Processing (ICIP), 2015.|
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.
|Computer vision journal list 2015|
|Computer Vision journal list|