Cutup and Detect: Human Fall Detection on Cutup Untrimmed Videos Using a
  Large Foundational Video Understanding Model
Till Grutschus Ola Karrar Emir Esenov Ekta Vats
This work explores the performance of a large video understanding foundation
model on the downstream task of human fall detection on untrimmed video and
leverages a pretrained vision transformer for multi-class action detection,
with classes: "Fall", "Lying" and "Other/Activities of daily living (ADL)". A
method for temporal action localization that relies on a simple cutup of
untrimmed videos is demonstrated. The methodology includes a preprocessing
pipeline that converts datasets with timestamp action annotations into labeled
datasets of short action clips. Simple and effective clip-sampling strategies
are introduced. The effectiveness of the proposed method has been empirically
evaluated on the publicly available High-Quality Fall Simulation Dataset
(HQFSD). The experimental results validate the performance of the proposed
pipeline. The results are promising for real-time application, and the falls
are detected on video level with a state-of-the-art 0.96 F1 score on the HQFSD
dataset under the given experimental settings. The source code will be made
available on GitHub.