English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work we propose an efficient method for activity recognition in a daily living scenario. At feature level, we propose a method to extract and combine low- and high-level information and we show that the performance of body pose estimation (and consequently of activity recognition) can be significantly improved. Particularly, we propose an approach extending the pictorial deformable models for the body pose estimation from the state-of-the-art. We show that including low level cues (e.g. optical flow and foreground) together with an off-the-shelf body part detector allows reaching better performance without the need to re-train the detectors. Finally, we apply the Fisher Kernel representation that takes the temporal variation into account and we show that we outperform state-of-the-art methods on a public dataset with daily living activities.

Daily Living Activities Recognition via Efficient High and Low Level Cues Combination and Fisher Kernel Representation