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One of the possible dangers that older people face in their daily lives isfalling. Occlusion is one of the biggest challenges of vision-based falldetection systems and degrades their detection performance considerably. Totackle this problem, we synthesize specifically-designed occluded videos fortraining fall detection systems using existing datasets. Then, by defining anew cost function, we introduce a framework for weighted training of falldetection models using occluded and un-occluded videos, which can be applied toany learnable fall detection system. Finally, we use both a non-deep and deepmodel to evaluate the effect of the proposed weighted training method.Experiments show that the proposed method can improve the classificationaccuracy by 36% for a non-deep model and 55% for a deep model in occlusionconditions. Moreover, it is shown that the proposed training framework can alsosignificantly improve the detection performance of a deep network on normalun-occluded samples.

Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body