People In Crowded Scenes Research Articles

SMART CROWD MONITORING SYSTEM USING IOT-BASED YOLO-GHOST

Internet of Things (IoT) devices offer a smart, sophisticated, real-time surveillance solution for public spaces. However, due to instantaneous lighting changes and varying viewing angles, counting and tracking the people in crowded scenes is a challenging problem. To combat these issues, a novel YOLO-CROWD is proposed for a smart crowd-monitoring system using YOLO-GHOST. Initially, an Internet Protocol (IP) based camera was used to monitor and capture crowds in a video sequence. The captured video sequences are converted into frames and passed to the server via the Internet. The recorded frames are given to the YOLO-GHOST classification module to perform people detection and counting. Finally, the detected output results are transferred to the surveillance monitoring center's server. The YOLO-CROWD technique is simulated by using MATLAB. The effectiveness of the proposed YOLO-CROWD technique is assessed using evaluation metrics such as accuracy, precision, recall, sensitivity, F1-score, and mean average precision. The experimental results show that the accuracy of the YOLO-CROWD has increased to up to 99.95 %, proving that its intended use is for accurate crowd detection. The detection accuracy of the proposed method is 84.9 %, 87.58 %, 93.91 %, and 97.72 % better than existing EABeD, LCDnet, CDEM-M, and Public Vision, respectively.

A three-stream fusion and self-differential attention network for multi-modal crowd counting

Multi-modal crowd counting aims at using multiple types of data, like RGB-Thermal and RGB-Depth, to count the number of people in crowded scenes. Current methods mainly focus on two-stream multi-modal information fusing in the encoder and single-scale semantic features in the decoder. In this paper, we propose an end-to-end three-stream fusion and self-differential attention network to simultaneously address the multi-modal fusion and scale variation problems for multi-modal crowd counting. Specifically, the encoder adopts three-stream fusion to fuse stage-wise modality-paired and modality-specific features. The decoder applies a self-differential attention mechanism on multi-level fused features to extract basic and differential information adaptively, and finally, the counting head predicts the density map. Experimental results on RGB-T and RGB-D benchmarks show the superiority of our proposed method compared with the state-of-the-art multi-modal crowd counting methods. Ablation studies and visualization demonstrate the advantages of the proposed modules in our model.

Scientific Exploration for Density Estimation and Crowd Counting of Crowded Scene

Crowd density estimation model is a typical concept which compute the counting of the people in the crowded image. There are many of research papers are written in this area to solve the different kind of real world problems. This paper shows the state of art and different types of framework proposed on density estimation and crowd counting techniques. The population of our country increases year by year, so the purpose of this paper is to explore different techniques available for crowd density estimation. There are lot of applications existing based on this technique like retail system, surveillance system etc., where the foot traffic is very vital in retail system to organize merchandise in aisles, optimize store layout, understand peak times and potentially even protect against theft. In surveillance system, this is mainly used for security purpose. In this, the researcher captures the crowd using the camera and the technique of counting the crowd which is divided into two parts unsupervised and supervised learning. These techniques are further divided into parts which is shown in the paper. The survey presents the different types of datasets of the crowded image which is useful for simulating the people counting in the crowded scenes. In detection based approach, this technique counts the people with the help of face detection technique. In this researchers face the problems of count the people in crowded scenes and also face the problem in surveillance applications. The cluster based approach data works well on the scattered crowd. This method needs image frames; it does not work on still images. In regression approach the model trained is subject to the point of view map. In the model were to be used in a different scene of a changed perspective map, it will have many inaccuracies in its result. The current research work intends to provide a general idea on crowd density estimation and counting approaches employed in visual surveillance in the perception of the study of computer vision. The analysis of review categorizes and frameworks quite a lot approximation related to crowd density and calculating approaches which are beneficial in providing the analysis related to the crowd places of interest.

Counting People by Estimating People Flows.

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it allows us to exploit the correlation between people flow and optical flow to further improve the results. We also show that leveraging people conservation constraints in both a spatial and temporal manner makes it possible to train a deep crowd counting model in an active learning setting with much fewer annotations. This significantly reduces the annotation cost while still leading to similar performance to the full supervision case.

Exploiting context for people detection in crowded scenes

The effective use of context information can significantly improve the effect of object detection. This paper proposes a method to exploit the context of a hard example searched by online hard example mining for improving the detection effect for people in crowded scenes. As shown by these experiments, this method can improve the effectiveness of faster R-CNN networks on people detection with a smaller convolutional neural network model. Because smaller convolutional neural network model is used, both the running memory consumption and the computing time can be reduced. Hence, our method can be implemented more easily on embedded devices.

Combining keypoint-based and segment-based features for counting people in crowded scenes

The counting of the number of people within a scene is a practical machine vision task, and it has been considered as an important application for security purposes. Most of the people counting algorithms generally extract the foreground segments and map the number of people to some features such as foreground area, texture, or edge count. Keypoint-based approaches, on the other hand, have also been proposed, which involves the use of statistical features of keypoints, such as the number of moving keypoints to estimate the crowd size. In contrast to the foreground segment-based methods, keypoint-based approaches are not sensitive to background changes, illuminations, occlusions, and shadows. However, they have limited performance due to the lack of sufficient features. In this paper, in order to estimate the crowd count, the combination of keypoint-based and segment-based (foreground) features is proposed. However, the whole approach is based on the keypoints and not all the image pixels. The proposed method, firstly, extracts the salient keypoints in the scene. Then, foreground segments are obtained by a simple morphological operation on the moving keypoints and hence the system does not suffer from difficulties associated with foreground/background segmentation. Various features are extracted from each foreground segment together with the corresponding keypoints which are highly correlated with the size, density, and occlusion level of the crowd. Finally, a combination of the segment-based and keypoint-based features is used to estimate the number of people in crowds. The experiment demonstrates that the proposed method achieves lower counting error rates compared to the existing approaches.

Estimation Of Number Of People In Crowded Scenes Using Amid And Pdc

Crowd density estimation in wide areas is a challenging problem for visual surveillance. So the safety of people in a crowds has been a major problem. Hence proposed a system to estimate the number of people in crowded scenes using AMID and PDC for wide-area surveillance . In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. By using multi-camera network, the camera can predict the crowd density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.

Estimation of number of people in crowded scenes using perspective transformation

In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images.