# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
###### Abstract  
Motion (change) detection is a basic preprocessing step in video processing, which has many application scenarios. One challenge is that deep learning-based methods require high computation power to improve their accuracy. In this paper, we introduce a novel semantic segmentation and lightweight-based network for motion detection, called Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation (MDNet-LBPC). In the feature extraction stage, the most computationally expensive CNN block is replaced with our single linear bottleneck operator to reduce the computational cost. During the decoder stage, our pooling compensation mechanism can supplement the useful motion detection information. To our best knowledge, this is the first work to use the lightweight operator to solve the motion detection task. We show that the acceleration performance of the single linear bottleneck is 5% higher than that of the linear bottleneck, which is more suitable for improving the efficiency of model inference. On the dataset CDNet2014, MDNet-LBPC increases the frames per second (FPS) metric by 123 compared to the suboptimal method FgSegNet_v2, ranking first in inference speed. Meanwhile, our MDNet-LBPC achieves 95.74% on the accuracy metric, which is comparable to the state-of-the-art methods.  
motion detection; single linear bottleneck; pooling compensation +
Footnote †: journal: Applied Sciences  
To solve the above efficiency problems in existing deep learning methods and improve the detection and segmentation efficiency of the model, we expect that we can accelerate the model by designing the compactness convolution structure.  
Some recent studies [21; 22; 23; 24] have shown that the bottleneck structure and deep separable convolution used in the lightweight network model can be used to reduce the complexity of the deep neural network and achieve a significant acceleration effect. Inspired by the recent success of semantic segmentation based motion detection method [9], we propose a Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation (MDNet-LBPC). This model classifies foreground and background pixels through the encoder decoder structure. Rich segmentation details and efficient segmentation can be obtained by the pooling compensation mechanism and single linear bottleneck structure. We successfully combine semantic segmentation techniques and the lightweight techniques of deep neural networks for efficient motion segmentation. Our main contributions are as follows:  
* We propose a novel real-time motion detection network based on single linear bottleneck operator and pooling compensation mechanism named MDNet-LBPC, which can achieve efficient motion detection with accurate segmentation results.
* Based on the bottleneck structure and the invert linear bottleneck operator, we propose a single linear bottleneck operator, which can reduce the FLOPs, thereby improving the inference efficiency of the model.
* We propose a pooling compensation technique based on attention mechanism and skip connections that can be used to compensate for the loss of information compression caused by pooling, thereby improving the accuracy of our motion detection model.