Crowd Behavior Recognition Research Articles

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

Crowd dynamics analysis and behavior recognition in surveillance videos based on deep learning

Crowd dynamics analysis and behavior recognition in surveillance videos based on deep learning

Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis

In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify crowd behavior by considering factors such as crowd size and violence levels can avert potential crowd-related disasters and hazards to a considerable extent. However, most existing approaches are not viable to deal with the complexities of crowd dynamics and fail to distinguish different violence levels within crowds. Moreover, the prevailing approach to crowd behavior recognition, which solely relies on the analysis of closed-circuit television (CCTV) footage and overlooks the integration of online social media video content, leads to a primarily reactive methodology. This paper proposes a crowd behavior detection framework based on the swin transformer architecture, which leverages crowd counting maps and optical flow maps to detect crowd behavior across various sizes and violence levels. To support this framework, we created a dataset comprising videos capable of recognizing crowd behaviors based on size and violence levels sourced from CCTV camera footage and online videos. Experimental analysis conducted on benchmark datasets and our proposed dataset substantiates the superiority of our proposed approach over existing state-of-the-art methods, showcasing its ability to effectively distinguish crowd behaviors concerning size and violence level. Our method’s validation through Nvidia’s DeepStream Software Development Kit (SDK) highlights its competitive performance and potential for real-time intelligent surveillance applications.Graphical abstract

Conditional autoregressive-tunicate swarm algorithm based generative adversarial network for violent crowd behavior recognition

Conditional autoregressive-tunicate swarm algorithm based generative adversarial network for violent crowd behavior recognition

Real-time crowd behavior recognition in surveillance videos based on deep learning methods

Automatic video surveillance in public crowded places has been an active research area for security purposes. Traditional approaches try to solve the crowd behavior recognition task using a sequential two-stage pipeline as low-level feature extraction and classification. Lately, deep learning has shown promising results in comparison to traditional methods by extracting high-level representation and solving the problem in an end-to-end pipeline. In this paper, we investigate a deep architecture for crowd event recognition to detect seven behavior categories in PETS2009 event recognition dataset. More especially, we apply an integrated handcrafted and Conv-LSTM-AE method with optical flow images as input to extract a high-level representation of data and conduct classification. After achieving a latent representation of input optical flow image sequences in the bottleneck of autoencoder(AE), the architecture is split into two separate branches, one as AE decoder and the other as the classifier. The proposed architecture is jointly trained for representation and classification by defining two different losses. The experimental results in comparison to the state-of-the-art methods demonstrate that our algorithm can be promising for real-time event recognition and achieves a better performance in calculated metrics.

Behavior Recognition Based on Category Subspace in Crowded Videos

Crowd behavior refers to a collective behavior composed of two or more individuals who influence, interact, and depend on each other for a specific goal. Compared with an ordinary crowd behavior, the probability of a dangerous crowd behavior is much smaller. Video-based crowd behavior recognition can be categorized as one multi-label classification task, which is characterized by complex scenes and imbalanced samples. Aimed at tackling problems of imbalanced samples and multi-label task, a classification method of associative subspace is proposed. For a single category (called main category) with fewer samples, this paper generates a special subspace wherein it is relatively easy to distinguish these samples by association with other categories. A classifier that can weaken the main category and strengthen relationship between the main category and other categories is designed in the subspace. Therefore, the main category can contribute to reducing dependence on the number of samples with the above-mentioned classifier in the corresponding subspace. In order to make full use of the relevant information concerning categories, multi-label information is further injected into spatio-temporal features of video action representation. Experiments on a challenging WWW dataset show that both the proposed subspace method and multi-label information fusion mechanism are efficient.

3D Res-Inception Network Transfer Learning for Multiple Label Crowd Behavior Recognition

3D Res-Inception Network Transfer Learning for Multiple Label Crowd Behavior Recognition

Optical Flow Approach Followed by SVM Classification Model to Recognize Abnormal Behavior of a Crowd

A many of researches have been carried out in the field of the crowd behavior recognition system. Recognizing crowd behavior in videos is most challenging and occlusions because of irregular human movement. This paper gives an overview of optical flow model along with the SVM (Support Vector Machine) classification model. This proposed approach evaluates sudden changes in motion of an event and classifies that event to a category: Normal and Abnormal. Geometric means of location, direction, and displacement of the feature points of each frame are estimated. Harris corner Detector is used in each frame for tracking a set of feature points. Proposed approach is very effective in real time scenario like public places where security is most important. After analyzing result ROC curve (receiver operating characteristics) is plotted which gives classification accuracy. We also presented frame level comparison with Ground truth and social force model (SFM) techniques. Our proposed approach is giving a promising result compare to all state of art methods.

Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness

Crowd motion segmentation and crowd behavior recognition are two hot issues in computer vision. A number of methods have been proposed to tackle these two problems. Among the methods, flow dynamics is utilized to model the crowd motion, with little consideration of collective property. Moreover, the traditional crowd behavior recognition methods treat the local feature and dynamic feature separately and overlook the interconnection of topological and dynamical heterogeneity in complex crowd processes. A crowd motion segmentation method and a crowd behavior recognition method are proposed based on streak flow and crowd collectiveness. The streak flow is adopted to reveal the dynamical property of crowd motion, and the collectiveness is incorporated to reveal the structure property. Experimental results show that the proposed methods improve the crowd motion segmentation accuracy and the crowd recognition rates compared with the state-of-the-art methods.

Behavior recognition and anomaly detection using edge devices on crowd behavior recognition system

Behavior recognition and anomaly detection using edge devices on crowd behavior recognition system

Hybrid tracking model and GSLM based neural network for crowd behavior recognition

Crowd behaviors analysis is the ‘state of art’ research topic in the field of computer vision which provides applications in video surveillance to crowd safety, event detection, security, etc. Literature presents some of the works related to crowd behavior detection and analysis. In crowd behavior detection, varying density of crowds and motion patterns appears to be complex occlusions for the researchers. This work presents a novel crowd behavior detection system to improve these restrictions. The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network. The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network. GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network. The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy. The experimentation results in the crowd behavior detection with a maximum accuracy of 93% which proves the efficacy of the proposed system in video surveillance with security concerns.

Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

In the current era, crowd behavior analysis is important topic due to the significance of video surveillance in the public area. Literature presents a handful of works for crowd behavior detection and analysis. Even though, the complicated challenges such as, low quality video, wide variation in the density of crowds and difficult motion patterns pose a complicated challenges for the researchers in crowd behavior detection. In order to alleviate these issues, we develop a crowd behavior detection system using hybrid tracking model and integrated features enabled neural network. The proposed crowd behavior detection system estimate the direction of movement of objects as well their activity using proposed GLM-based neural network. The proposed GLM-based neural network integrates the LM algorithm with genetic algorithm to improve the learning process of neural network. The performance of the proposed crowd behavior detection algorithm is validated with five different video and the performance is extensively analyzed using accuracy. From research outcome, we proved that the proposed system obtained the maximum accuracy of 95% which is higher than the existing methods taken for comparison.

ディープラーニングを利用した群集動態認識システムの検討

The crowd behavior recognition system is a subsystem of a distributed cooperative dynamic evacuation guidance system, and it detects and predicts crowd flow and abnormality occurrence by using machine learning method such as deep learning with visual and depth information obtained by RGB-D camera. The distributed cooperative dynamic evacuation guidance system aims to suggest evacuation routes at extensive evacuation situations by autonomously cooperation among plural sensors and evacuation guiding devices. In this paper, a recognition method of overall behavior of the crowd is proposed. Some indices for indicating the situation are examined in order to recognize the overall behavior of the crowd flow and the abnormality occurrence, and cooperate among the system by sharing the recognition results rapidly.

High Density Crowd Behaviors Recognition Based on Micro-behavioral and Sparse Representation

This paper presents a novel method to recognize high density crowd behaviors using micro-behaviors combining with Sparse Representation based on Locally Linear Embedding (named LLE-based Sparse Representation or LLE-SR). We extract micro-behaviors from each frame, respectively named Fountainhead, Bottleneck, Blocking, Lane and Ring/Arch, and construct micro-behaviors histograms to better describe complex high density crowd scenes. As the mid-lever semantic, micro-behaviors solve the gap between the high-level semantics and low-level semantic, and the creation of them does not need any information of target track. LLE-SR method fully considers the behavior of local manifold structure of samples. Through adding LLE regularization term in sparse classification model, the unstable property of the manifold structure can be solved, and then recognition rate is improved. Numerous experiments have been conducted in real scenes, the results of which demonstrate the effectiveness and robustness of the proposed method for high density crowd behavior recognition.

基于因果网络分析的小规模人群行为识别

基于因果网络分析的小规模人群行为识别

High-density Crowd Behavior Recognition Based on Improved Harris

High-density Crowd Behavior Recognition Based on Improved Harris