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Wearable cameras stand out as one of the most promising devices for theupcoming years, and as a consequence, the demand of computer algorithms toautomatically understand the videos recorded with them is increasing quickly.An automatic understanding of these videos is not an easy task, and its mobilenature implies important challenges to be faced, such as the changing lightconditions and the unrestricted locations recorded. This paper proposes anunsupervised strategy based on global features and manifold learning to endowwearable cameras with contextual information regarding the light conditions andthe location captured. Results show that non-linear manifold methods cancapture contextual patterns from global features without compromising largecomputational resources. The proposed strategy is used, as an application case,as a switching mechanism to improve the hand-detection problem in egocentricvideos.

Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos