Crowd Motion Detection Research Articles

Crowd Analysis System for Images of CCTV Camera

It is important to monitor and analyze the crowd for peaceful event organization and minimum number of persons killed or injured in a stampede, war or accident in public or religious places. Ultimately, crowd management strategies should be adopted for crowd safety. Detecting the abnormal crowd behaviour is the interesting research area. Traditionally to monitor the crowd closed circuit television is used. Video analyses are used in many application areas. This work is a paper of traditional approach and is a study of different attributes of crowd which include crowd counting and estimation of density, detection of crowd motion. It also presents the study about behaviour understanding of crowd and crowd tracking. We have also presents crowd analysis using different deep learning methods. This paper showed results of crowd analysis using convolutional neural network and different recurrent neural network training model.

Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy.

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.

Abnormal crowd motion detection using double sparse representation

The sparse representation method is widely used in the area of abnormal crowd motion detection to accurately represent the crowd motions with high dimension features. To overcome its lack of training samples and achieve more accurate detection, a double sparse representation method with a dynamic dictionary updating process is proposed. The proposed method utilizes two sparse representation classifiers that each gives a separate judgment for every test sample. Fuzzy integral is also employed to detect any abnormality in a sample. The results of experiments conducted on various datasets show that the proposed method achieves higher accuracy than state-of-the-art methods in local and global abnormal events detection.

Abnormal Crowd Motion Detection with Hidden Conditional Random Fields Model

Crowd motion analysis in public places is an important research subject in the monitoring field. This paper proposes an approach for detecting abnormal crowd motion using Hidden Conditional Random Fields Model (HCRF). This approach derives variations of motion patterns from direction distribution of the crowd motion obtained by the optical flow and these variations are encoded with HCRF to allow for the detection of abnormal crowd motion. Modeling the temporal neighborhood relations in a video sequence based on HCRF can incorporate hidden states and label the video depending on long range observations. The experimental results show that this proposed algorithm can achieve better results than HMM and CRF.

Abnormal Crowd Motion Detection with Hidden Markov Model

stations,etc. With the increasing demand of surveillance of various human activities, an efficient automated surveillance system to detect anomalies has become important. There is a survey on visual surveillance in [1], and a lot of problems have not resolved in surveillance applications nowadays as discussed in some papers [2]. Crowd feature extraction and crowd modeling are two important approaches of crowd analysis in Video surveillance. Most detection methods use motion features such as image gradient, texture information, optical flow, or spatial-temporal volume characteristics [3-4]. These features allow us to analyze dense crowds using characteristics of a crowd rather than individuals. Some works in the analysis of crowds usually assume that individuals can be tracked and identified in the crowd [5]. For example, the method proposed in [6] combines the inter-images difference based on entropy image and optical flow computed by a local method with a hierarchical coarse-to-fine optical flow estimation, and this method has a big computation load. Once motion features are extracted, another approach is to model them to represent normal motion behaviors. Abnormal crowd motion is defined as a sudden change or irregularity in a crowd motion. Suppose there exists a mathematical model describing the normal behaviors in the crowd videos, anomaly detection is done by modeling normal behaviors, and a crowd behavior is declared as anomaly if its characteristic does not comply with the learning model. Many approaches use parametric models, however, in parametric approach the data characteristics have to be approximated with a standard distribution and in many cases such approximations do not work well. In [7], Social Force model is used to detect and localize abnormal behaviors in crowd videos by Mehran et al. Their result shows the approach has a better performance than similar approach based on pure optical flow. Andrade et al. [8] combined spectral clustering, Principal Components Analysis (PCA) and Hidden Markov Model (HMM) to detect the crowd emergency scenarios, but the eigenvectors obtained by dimensionality reduction with PCA on the optical flow fields can not adequately reflect the motion, and this approach was only tested in simulated data. Our approach derives variations of motion patterns though direction distribution of the crowd motion obtained by optical flow and these variations are encoded with HMM Abnormal Crowd Motion Detection with Hidden Markov Model Dongping Zhang, Yafei Lu, Xinghao Jiang, Huailiang Peng