计算机应用与软件2024，Vol.41Issue(1)：89-96,145,9.DOI:10.3969/j.issn.1000-386x.2024.01.014
基于轻量级MobileNet-SSD模型的人流量检测
HUMAN TRAFFIC DETECTION BASED ON LIGHTWEIGHT MOBILENET-SSD MODEL
摘要
Abstract
Using deep neural network models to identify and detect pedestrian targets has very high value.In the real high-density pedestrian detection scene,due to the impact of hardware foundation and network performance consumption,it is often necessary to select a network with high processing speed and low hardware requirements,while taking into account the continuous characteristics of video surveillance.Therefore,this paper selected the lightweight MobileNet-SSD network to efficiently process human head targets and introduced the method of inter-frame difference to effectively track the elliptical feature targets of the human head.The related mathematical methods were combined to achieve a high-performance pedestrian flow detection solution that counted pedestrians across the line.After comparing with the current first-class detection models on different data sets,the proposed method showed excellent detection performance.关键词
MobileNet-SSD/帧间差分/跨线计数/轻量级网络Key words
MobileNet-SSD/Inter-frame difference/Cross-line count/Lightweight network分类
信息技术与安全科学引用本文复制引用
张智,盛健..基于轻量级MobileNet-SSD模型的人流量检测[J].计算机应用与软件,2024,41(1):89-96,145,9.基金项目
国家自然科学基金项目(61673304) （61673304）
国家社会科学基金重大计划项目(11&ZD189). （11&ZD189）
