Section: Overall Objectives

Highlights of the Year

Stars designs cognitive vision systems for activity recognition based on sound software engineering paradigms.

This year, we have designed several novel algorithms for activity recognition systems. In particular, we have extended an efficient algorithm for detecting people in a static image based on a cascade of classifiers. We have also proposed a new algorithm for re-identification of people through a camera network. This algorithm outperforms state-of-the-art approaches on several benchmarking datasets (e.g. Ilids). We have realized a new algorithm for the recognition of short actions and validated also its performance on several benchmarking databases (e.g. ADL). We have improved a generic event recognition algorithm by handling event uncertainty at several processing levels. We have extended an original work on learning techniques such as data mining in large multimedia databases based on offline trajectory clustering. We have designed a generic controller algorithm, which is able to automatically tune the parameters of tracking algorithms.

We have also continued a large clinical trial with Nice Hospital to characterize the behaviour profile of Alzheimer patients compared to healthy older people.

We have organized a summer school which was held at Inria in October 2012, entitled “Human Activity and Vision Summer School", with many prestigious researchers (e.g. M. Shah).