Summary

The main topic of my research is video analysis for automatic detection and recognition of human motion. This includes computer algorithms that can automatically find people in real-life video sequences and reason about what they are doing. My work usually involves techniques from image processing, statistics and machine learning.

List of publications

A complete list of publications to date can be found here.

Projects

Extracting Moving People from Internet Videos

We propose a fully automatic framework to detect and extract arbitrary human motion volumes from real-world videos collected from the Internet. We demonstrate the initial success of this framework both quantitatively and qualitatively by using a number of downloaded YouTube videos.

Related publications:

  • Juan Carlos Niebles, Bohyung Han, Andras Ferencz and Li Fei-Fei. Extracting Moving People from Internet Videos. To appear in the 10th European Conference on Computer Vision, Marseilles, France, 2008. PDF. Project Page. Code.

A Hierarchical Model of Shape and Appearance for Human Action Classification

We present a novel model for human action categorization. A video sequence is represented as a collection of spatial and spatial-temporal features by extracting static and dynamic interest points. We propose a hierarchical model that can be characterized as a constellation of bags-of-features and that is able to combine both spatial and spatial-temporal features.

Related publications:

  • Juan Carlos Niebles and Li Fei-Fei. A Hierarchical Model of Shape and Appearance for Human Action Classification. CVPR 2007. Minneapolis, USA.

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

Can computers automatically identify different human activities in a video? We address the problem by representing a video sequence as a "bag of video words", and applying a generative probabilistic model to learn and recognize different human actions in video. In the figure, the approach is used to classify three different motions from figure skating.

There is a description and some resources available at our Project Page.

Related publications:

  • Juan Carlos Niebles, Hongcheng Wang and Li Fei-Fei. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. International Journal of Computer Vision. In press. 2008.
  • Juan Carlos Niebles, Hongcheng Wang and Li Fei-Fei. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. British Machine Vision Conference (BMVC), Edinburgh, 2006. - Oral Presentation.
  • Juan Carlos Niebles, Hongcheng Wang and Li Fei-Fei. Unsupervised Learning of Human Action Categories. In Video Proceedings CVPR, New York, 2006.

Collaborators

Li Fei-Fei, Hongcheng Wang, Silvio Savarese, Andrey Del Pozo, Jia Li, Bohyung Han, Andras Ferencz, Bangpeng Yao and everyone from the Stanford Vision Lab.