本科毕设：监控视频下的人数统计
核心点：crowd counting
目前主要的实现方法: counting by detection,counting by clustering, and counting by regression.
counting by detection：
解决思路：detection+tracking
主要问题：detector在复杂高密度人群中性能差；time-consuming
counting by clustering：
解决思路：motion cluster
主要问题：要求视频帧率足够支持motion分析；
counting by regression：
解决思路：将低维特征映射到people count
主要问题：映射的精度；
综上：counting by detection是比较直观的做法，但跑了demo之后结果不甚满意；目前我比较中意 counting by regression的解决思路，也在着手阅读相关的论文。
测试数据：
目前下载的几个数据库预览：
UCSD行人 PETS2009 PETS2002 mall
UCSD行人库和PETS2009均为室外库，分辨率较好，直接跑human detector效果也是稳定的；
PETS2002，mall为室内库，分辨率较差，直接跑human detector（RCNN）效果比较差；【可能下周能从项目上拿到其他的零售店的video】
现在看的论文很多都是针对特定场景训练的，目前也不能做跨场景的，所以尽快确定场景，才能尽快开始工作。
state of art：ICCV15（还没有调研完全）
测试标准： mean absolute error (mae), mean squared error (mse), and mean deviation error (mde)
相关工作：
•ICCV15:Bayesian Model Adaptation for Crowd Counts
• Trans on image processing12:Counting people with low-level features and Bayesian regression
•ICCV09:Bayesian Poisson regression for crowd counting
•Cvpr08:Privacy preserving crowd monitoring: Counting people without people models or tracking
•AVSS12:People Count Estimation In Small Crowds
•ICCV13:From Semi-Supervised to Transfer Counting of Crowds
•ICCV15:COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation
•ICIP14:CROWD ANALYSIS IN NON-STATIC CAMERAS USING FEATURE TRACKING AND MULTI-PERSON DENSITY
•CVPR15:Person Count Localization in Videos from Noisy Foreground and Detections
•CVPR15:Cross-scene Crowd Counting via Deep Convolutional Neural Networks
•CVPR13:Cumulative Attribute Space for Age and Crowd Density Estimation
•BMVC12:Feature Mining for Localised Crowd Counting
相关研究组：
https://scholar.google.com/citations?hl=en&user=Fykyo9gAAAAJ&view_op=list_works&sortby=pubdate
http://personal.ie.cuhk.edu.hk/~ccloy/