基于SSD卷积神经网络的公交车下车人数统计

传统典型的公交车人数统计方法在准确率和速度方面存在一些不足，且提取目标特征的效果较差.本文提出了基于深度卷积神经网络的公交车人数统计系统解决人群计数问题.首先制作数据集，难点在于所有用于训练的数据集均是手工标注.并且公交车摄像头角度比以往文献覆盖更广区域.本文首先比较了多种不同的深度卷积神经网络模型对乘客进行全身检测的效果.综合考虑检测速率、准确率等方面，最终采用单次检测器深度卷积神经网络模型对乘客进行人头目标检测，在线实时目标追踪算法实现人头的多目标追踪，跨区域人群计数方法统计公交车下车人数.系统准确率达到78.38%，运行速率约为每秒识别19.79帧.实现了人群计数.

Statistics on Number of People Getting off Bus Based on SSD Convolutional Neural Network

The statistics of traditional and typical bus passengers have some shortcomings in accuracy and speed, and the effect of extracting target features is poor. This study proposes a bus counting system based on deep convolutional neural network to solve the crowd counting problem. The first thing to make a dataset is that all the datasets used for training are hand-labeled. And the bus camera angle is wider than the previous literature. This study first compares the effects of various deep convolutional neural network models on the whole body detection of passengers. Considering the detection rate and accuracy, the single-detector deep convolutional neural network model is used to detect passengers'' heads. The simple online and real-time target tracking algorithm implements multi-target tracking of human heads, and the cross-region crowd counting method is used to count the number of passenger getting off the bus. The system accuracy rate reaches 78.38% and the operating rate is approximately 19.79 frames per second. the passenger count is achieved.