468 results about "Crowd density" patented technology

A system and method for automated and / or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

The invention discloses a crowd counting method based on a deep residual network. The method applies the deep residual network to extract the characteristic of each frame of image in a crowd monitoring video, wherein the input of the deep residual network is one frame of image; through 5*5 kernel convolution and pooling, an initial characteristic graph is obtained; through ten residual network units, characteristics are extracted; a main branch obtains a crowd density graph corresponding to an input image through 1*1 kernel convolution; an auxiliary branch obtains a people number corresponding to the input image through the 1*1 kernel convolution; and finally, through the integration of the crowd density graph, the people number estimation value of the input image is obtained. Each residual network unit has the structure that a 3*3 conventional kernel is accessed after a 1*1 convolution kernel, then, the 1*1 convolution kernel is accessed, a batch normalization operation and a linear rectification operation are added after each convolution kernel, and meanwhile, the output of a previous residual network unit also serves as the input of a next residual network unit through the 1*1 kernel convolution. By use of the method, an influence on crowd counting by scene transformation can be reduced, and a stable crowd counting effect is obtained.

The invention discloses a multiscale full convolutional network feature fusion-based crowd counting method. The method comprises the following steps of: inputting a picture and respectively entering three branch networks so as to obtain feature maps with different scales; fusing the obtained feature maps of the branch networks so as to obtain a finally estimated feature map; mapping the output feature map into a density map; and carrying out summation on the density map to realize estimation of the current crowd number. The multiscale full convolutional network feature fusion-based crowd counting method provided by the invention is capable of overcoming severe influences, on crowd counting, of shielding, scene perspective distortion and different crowd distributions, has relatively strongpracticability and good robustness, is capable of accurately carrying out crowd counting or crowd density estimation, and has an important value for monitoring crowd abnormal events.