Abstract: Because the performance of most existing crowd counting methods isdecreased when they are applied to a new scene,a crowd counting method based on non-parameter tuning was proposed in the framework of multilayer BP neural network.Firstly,image blocks are cropped from the training images to obtain similar scale pedestrian as an input of crowd BP neural network model.Then,the predictive density map is learned by BP neural network model to obtain representative crowd blocks.Finally,in order to deal with the new scene,the target scene is adjusted on the trained BP neural network model,retrieving samples with the same attributes,which includes candidate block retrieval and local block retrieval.The data set includes PETS2009 data set,UCSD data set and UCF_CC_50 data set.The effectiveness of the proposed method is verified by the experimental results on these scenes.Compared with the global regression coun-ting method and density estimation counting method,the proposed method has advantages of average absolute error and mean square error,and overcomes the influences of the differences between the scenes and foreground segmentation.

Key words: Average absolute error, BP neural network, Crowd counting, Density map, Non-parameter tuning