计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 229-235.doi: 10.11896/j.issn.1002-137X.2018.08.041
郭文生, 包灵, 钱智成, 曹万里
GUO Wen-sheng, BAO Ling, QIAN Zhi-cheng, CAO Wan-li
摘要： 基于监控视频的人数(人群)统计是人群行为的分析、资源的优化配置、现代安防、商业信息的采集以及智能管理等重要任务的基础,具有较高的研究意义与应用价值。近年来,数字图像处理技术以及深度学习理论的不断完善和发展,极大地促进了基于监控视频的人数统计的研究,但仍然无法很好地解决监控场景中人数统计准确率较低、高清图片耗时的问题。针对在待检对象尺度变化较大的情况下,基于对象检测的人数统计方法的准确率大幅下降的问题,提出一种基于自适应叠合分割与深度神经网络的人数统计方法。该方法的思想来源于注意力机制,同时充分利用了叠合分割块内人头对象的尺度信息和人数信息。实验结果表明,自适应叠合分割算法能够与现有深度神经网络对象检测模型相结合,并且相较于直接利用深度神经网络对象检测模型进行人数统计的方法,该结合方法可以大幅提高人数统计的准确率。
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