Human fall detection based on improved particle swarm optimization algorithm and neural network

As the global population continues to age, fall detection has become a common concern in the field of public safety. Fast and accurate detection of falling behaviors in surveillance videos and timely sending out help signals can effectively reduce the injuries caused by falls in the elderly. This paper proposes a hybrid algorithm based on an improved particle swarm optimization algorithm and a neural network for real-time fall detection in indoor environments. Human keypoints in video frames are first extracted using the alphapose model, and then the human keypoints are classified in real-time using an improved particle swarm optimization neural network model. Experimental results show that this method can effectively detect falling behaviors in indoor scenes.