  One of the possible dangers that older people face in their daily lives is
falling. Occlusion is one of the biggest challenges of vision-based fall
detection systems and degrades their detection performance considerably. To
tackle this problem, we synthesize specifically-designed occluded videos for
training fall detection systems using existing datasets. Then, by defining a
new cost function, we introduce a framework for weighted training of fall
detection models using occluded and un-occluded videos, which can be applied to
any learnable fall detection system. Finally, we use both a non-deep and deep
model to evaluate the effect of the proposed weighted training method.
Experiments show that the proposed method can improve the classification
accuracy by 36% for a non-deep model and 55% for a deep model in occlusion
conditions. Moreover, it is shown that the proposed training framework can also
significantly improve the detection performance of a deep network on normal
un-occluded samples.
