Population Statistics Algorithm Based on MobileNet

Abstract

In today’s society, intelligent video surveillance plays an important role in social security, traffic scheduling, national security and other fields. One of the research hotspots is people statistics based on image processing, which has strategic significance in practical applications. Aimed at the problem that the low accuracy in the actual application scenario, the limited hardware resources, and the low operation efficiency, this paper proposes a multi-feature target detection model based on the lightweight deep learning network MobileNet [1], which can be used in intelligent terminals. The basic feature-extraction network MobileNet as a lightweight network can provide a flexible alternative configuration in terms of efficiency and accuracy. The underlying detection network selects a single deep nerual network, named SSD [2]. The algorithm can achieve multi-scale target detection, and uses the target position and category to perform one-time regression. In this paper, the activation function of SSD is changed into SeLU (scaled exponential linear units) [3], which improves the robustness of the algorithm. At the same time, the work of sample diversity and data enhancement has been made, and the characteristics of the human body above the shoulders have been fully utilized. Experiments have shown that the improved network structure based on MobileNet has higher detection accuracy, lower delay, excellent robustness, while the number of model parameters is effectively reduced.