Human fall detection based on multi-algorithm fusion

Abstract

In response to the problems of complex implementation, low accuracy, poor applicability and high latency of some contemporary human fall detection algorithms that do not achieve real-time results, this paper proposes a fall detection based on Tiny-YOLO target detection algorithm, Kalman target trajectory tracking, Alphapose human pose recognition and spatio-temporal convolutional network, a combination of multiple algorithms. It solves the problem that human target features are difficult to extract and subsequent tracking is lost. And the training speed is accelerated to achieve fast detection of human falls through video and improve the accuracy. The algorithm is tested on the publicly available datasets UR Fall Detection Dataset and Lei2 fall Detection Dataset, and the experimental results prove that the algorithm in this paper has high detection accuracy and effectively reduces the false detection rate.