Research

Video Understanding

Video understanding is concerned with the parsing of the image data flow for the semantic understanding of the objects in the scene, but also their actions a...

Human Interactions

Recognizing human interactions from video is an important step forward towards the long-term goal of performing scene understanding fully automatically. Rece...

Person Re-Identification

Long-duration tracking of individuals across large sites remains an almost untouched research area. Trucks of individuals acquired in disjoint fields of view...

Transfer Learning from Multiple Sources

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...

Video Surveillance

Increasingly, large networks of surveillance cameras are employed to monitor public and private facilities. This continuous collection of imagery has the pot...

Aerial Video Analysis

In aerial video moving objects of interest are typically very small, and being able to detect them is key to enable tracking. There are detection methods tha...

People Detection

People detection and tracking in video are fundamental Computer Vision capabilities that still constitute a research challenge. Important difficulties are du...

Dynamic Textures

One of the most important elements of modern Computer Vision is the concept of image texture, or simply texture. Depending on the task at hand (e.g. image-ba...

Dynamic Texture Segmentation

Segmenting the image plane of video sequences is often one of the first steps towards the analysis of video. A lot of effort has been spent on developing ima...

Dynamic Texture Recognition

Recognition of objects based on their images is one of the central problems in modern Computer Vision. Objects can be characterized by their geometric, photo...

Resources

HAUS Dataset

Repository of the Human Activities Under Surveillance video dataset.

Posts

Publications

  1. AVSS
    A model change detection approach to dynamic scene modeling Kim, S. J., Doretto, G., Rittscher, J., Tu, P., Krahnstoever, N., and Pollefeys, M. In IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. Oral abstract bibTeX pdf
  2. Event recognition with fragmented object tracks Chan, M., Hoogs, A., Sun, Z., Schmiederer, J., Bhotika, R., and Doretto, G. Technical Report 2006GRC038, GE Global research, 2006. Visualization and Computer Vision Laboratory abstract bibTeX