###### Abstract  
In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3 DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and UCF101 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models. CNN; action recognition; spatial pyramid pooling; dense connectivity; 3D convolution +
Footnote †: journal:  
0000-0001-4500-387X][PERSON] _et al._[1][PERSON], _et al._[2][PERSON], _et al._[3][PERSON], _et al._[4][PERSON], _et al._[5][PERSON], _et al._[6][PERSON], _et al._[7][PERSON], _et al. further improved the two-stream network by using the input combination of RGB, optical flow, and warped optical flow in addition to the the network structure using BN-inception. To improve network accuracy by deepening the network layers, ResNets [7] and Highway Networks [8] bypass signals from one layer to the next via identity connections. Stochastic depth [9] shortens ResNets by randomly dropping layers during training to allow for better information and gradient flow. FractalNets [10] repeatedly combine several parallel layer sequences with different numbers of convolutional blocks to obtain a large nominal depth while maintaining many short paths in the network. In fact, a direct method of action recognition using deep networks is to use temporal information to arm the convolutional operation. To achieve this, [PERSON] et al., [11] presented a three-dimensional (3D) convolutional network using 3D convolution operations to extract features from spatial and temporal dimensions to capture spatiotemporal information between adjacent frames. Although the 3D convolutional network has a simpler design idea, it generates a large number of intermediate parameters, which increases the complexity of the network. Recently, DenseNets [12] have attracted significant attention from computer vision researchers due to their dense connections. This network has shown impressive performances in natural image classification tasks. The network connects each layer to any other layer in a feed-forward manner. This dense connection structure can facilitate gradient flow during training. Compared with the 3D convolutional neural network, the dense connection of DenseNets reduces the repeated calls of intermediate parameters, which can reduce a large number of network parameters, thus reducing the complexity of the network. However, DenseNets have only been applied in terms of images; they have not performed well in the video field. It is well known that deep convolutional neural networks (DCNN) [13] require a fixed-size image input. This requirement may affect the recognition accuracy for the images or sub-images of an arbitrary size/scale. However, in reference [14], the authors present a spatial pyramid representation method. In this method, the fixed-size constraint can be removed by using the spatial pyramid pooling (SPP) model. Inspired by the application of SPP in the image field, the image cropping is reduced, thereby reducing the loss of image information. Can the video field also reduce such information loss? What would be the effect of applying SPP to the video world?