Crowd Scene Analysis Using Deep Learning Network

Abstract

Crowd scene analysis is the certification tasks in crowded scene understanding. Crowd is a same or different set of people arranged in one group. Generally crowd form in the way of pedestrians, supermarket, and marathons. In this paper introduce Convolution Neural Networks and deep learning model is used for the analysis of crowd scene. In these paper propose, findings the number of people arrived in one group and also finds the crowd density map. People counting in extremely dense crowds are an important step for video surveillance and anomaly warning. The above mentioned works, Several problems becomes especially more challenging due to the lack of training samples, severe blockages, disorder scenes, and modification of perspective. In existing methods estimating crowd count using handcrafted features such as SIFTS and HOG. In current vision most suited method is to predict the better performance of estimating crowd density and crowd count based on deep learning network. Lucas kanade optical flow can finds the displacement vector between two consecutive frames. 3D volumes video slices can be arranged in sequential manner. In this crowd scene analysis represents convolutional crowd dataset as 100 videos from 800 crowd scenes and build an attribute set with 94 attributes.