Falling behavior will bring serious injury to the elderly, especially to the elderly living alone. How to correctly identify the falling behavior and issue a warning is an important factor for reducing the injury. A fall detection algorithm is proposed, which is based on TSSI (tree structure skeleton image) and learnable STB-CNN (spatio-temporal block convolutional neural network) . Firstly, human joint point is extracted by the three-dimensional pose estimation algorithm, and the corresponding skeleton sequence can be obtained. Secondly, the skeleton sequence is encoded into TSSI by the algorithm based DFS (depth first search) method. Finally, a learnable STB-CNN is proposed to classify TSSI and detect the fall behavior, which consists of spatio-temporal difference module, learnable spatio-temporal framework and spatio-temporal multi-branch convolution module, . Experiments are carried out on the public datasets UR FALL Detection Datasets and the simulation datasets. Experimental results are shown that our fall detection algorithm is more accurate than other related algorithms, especially the accuracy to 98.6% and 98.3% respectively.

An unsupervised generative adversarial network is proposed to solve the problems of boundary blurring and information loss in the focusing and defocusing regions of existing multi-focus image fusion methods. By constructing a generator with complex attention feature extraction module, the global and local features of the source image can be fully extracted and the learning of image color information can be strengthened. The combined gradient of the source image is used as the input of the discriminator to enhance the extraction of texture details. Combined with structural similarity and peak signal-to-noise ratio, the structure perception loss is proposed to further improve the quality of the fused image. The experimental results of Lytro data set show that, compared with 7 representative fusion algorithms, this method achieves good fusion performance in both subjective and objective evaluation, among which the indices PSNR, AG, SF, and EI reach 52.38, 8.25, 22.74, and 85.96, respectively, representing improvements of 5.5%, 2.2%, 1.4%, and 2.1% over the second-best algorithm.