Crowded Scene Analysis: A Survey

Computer vision based crowd disaster avoidance system: A survey

In this chapter we first review the recent studies that have begun to address the various challenges associated with the analysis of crowded scenes. Next, we describe our two recent contributions to crowd analysis in video. First, we present a crowd analysis algorithm powered by prior behaviors that are learned on a large database of crowd videos gathered from the Internet. The proposed algorithm performs like state-of-the-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individuals behave in an unusual way. Second, we address the problem of detecting and tracking a person in crowded video scenes. We formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. The proposed methods are validated on a challenging video dataset of crowded scenes. Finally, the chapter concludes by describing ongoing and future research directions in crowd analysis.

Chapter 14 - Crowd behavior analysis from fixed and moving cameras

Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the attention of researchers in video surveillance from individual behavior analysis to group and crowd behavior analysis. However, dangerous and illegal behaviours are mostly occurred from groups of people. Group detection is the main process to separate people in crowded scene into different group based on their interactions. Results of group detection can further to apply in analyze group and crowd behaviour. This paper present a study of the group detection and propose a novel approach for clustering group of people in different crowded scenes based on trajectories. For the clustering of group of people we propose novel formula to compute the weights based on the distance, the occurrence, and the speed correlations of two people in a tracklet cluster to infer the people relationship in a tracklet clusters with Expectation Maximization (EM) in order to overcome occlusion in crowded scenes.

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

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.

Continuous monitoring and automatic detection of specific crowd activities such as dispersion and congestion; is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scenes is a critical issue as it typically involves the poor resolution of objects, occlusions, and complex dynamics. In this paper, we propose a systematic, novel, and unsupervised method based on global motion analysis of people, to detect dispersion and merging events in crowded scenes. We avoid tracking of individual person as well as the use of any trained classifier while detecting the event. Our approach is tested on standard datasets as well as our own dataset. The results show the efficacy of our approach.

Since last decade, crowd behaviour analysis and management gained lots of consideration from the researchers for the intelligent video systems. Automated surveillance systems faces challenges in crowd behaviour modeling and analysis because of dynamic characteristics of crowd and individuals. In this paper we propose a new approach for the detection and analysis of crowd behaviour by using adaptive swarm intelligence and optical flow estimation based approach. According to this approach, initially image is modelled to generate the optical flow. This modeled image contains foreground, background and image region (higher intensity). Optical flows and streaklines are used to represent motions observed. The observed motions are analyzed using particle swarm optimization. The simulation study is carried out on the publicly available dataset from University of Minnesota using MATLAB simulation tool. Experimental study shows that the proposed approach is more efficient when compared to the existing approach for the detection and behaviour analysis. Comparative study is carried out in terms of classification error and area under curve.

Population growth has made the probability of incidents at large-scale crowd events higher than ever. In the past decades, automated crowd scene analysis done by computer vision has attracted attention. However, severe occlusions and complex crowd behaviors make such analysis a challenge. As a key aspect of crowd scene analysis, a number of works dealing with dense crowd anomaly detection based on computer vision have been presented. This work is a survey of computer vision techniques for analyzing dense crowd scenes. It covers two aspects: crowd density estimation and abnormal event detection. Some problems and perspectives are discussed at the end.

The analysis of crowded scenes is one of the most challenging scenarios in visual surveillance, and a variety of factors need to be taken into account, such as the structure of the environments, and the presence of mutual occlusions and obstacles. Traditional prediction methods (such as RNN, LSTM, VAE, etc.) focus on anticipating individual’s future path based on the precise motion history of a pedestrian. However, since tracking algorithms are generally not reliable in highly dense scenes, these methods are not easily applicable in real environments. Nevertheless, it is very common that people (friends, couples, family members, etc.) tend to exhibit coherent motion patterns. Motivated by this phenomenon, we propose a novel approach to predict future trajectories in crowded scenes, at the group level. First, by exploiting the motion coherency, we cluster trajectories that have similar motion trends. In this way, pedestrians within the same group can be well segmented. Then, an improved social-LSTM is adopted for future path prediction. We evaluate our approach on standard crowd benchmarks (the UCY dataset and the ETH dataset), demonstrating its efficacy and applicability.

Scale-aware CNN for crowd density estimation and crowd behavior analysis

Crowd scene analysis receives growing attention due to its wide applications. Grasping the accurate crowd location is important for identifying high-risk regions. In this article, we propose a Compressed Sensing based Output Encoding (CSOE) scheme, which casts detecting pixel coordinates of small objects into a task of signal regression in encoding signal space. To prevent gradient vanishing, we derive our own sparse reconstruction backpropagation rule that is adaptive to distinct implementations of sparse reconstruction and makes the whole model end-to-end trainable. With the support of CSOE and the backpropagation rule, the proposed method shows more robustness to deep model training error, which is especially harmful to crowd counting and localization. The proposed method achieves state-of-the-art performance across four mainstream datasets, especially achieves excellent results in highly crowded scenes. A series of analysis and experiments support our claim that regression in CSOE space is better than traditionally detecting coordinates of small objects in pixel space for highly crowded scenes.

Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.

Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps using unsupervised hierarchical clustering algorithm. The hybrid generative-discriminative feature maps are derived by posterior divergence based on the tracklets which are captured by tracking dense points with three effective rules. The feature maps effectively associate low-level features with the semantical motion patterns by exploiting the hidden information in crowded scenes. Motion pattern analyzing is implemented in a completely unsupervised way and the feature maps are clustered automatically through hierarchical clustering algorithm building on the basis of graphic model. The experiment results precisely reveal the distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.

This paper presents a novel approach to detecting crowd groups and learning semantic regions with a Gestalt laws-based similarity. Different from the existing approaches based on optical flows or complete trajectories, our model adopts tracklets as the original input, because they carry more detailed information. Though those tracklets do not appear in the same duration, they are more robust to noise in crowd scene. According to the Gestalt laws of grouping, we propose three priors to define a unified similarity measure to calculate the affinities of pairs of original tracklets and pairs of representative tracklets in crowd groups. Therefore, the short-term crowd groups and the long-term semantic paths in crowded scene can be detected by a bottom-up hierarchical clustering algorithm simultaneously. Extensive experiments on hundreds of video clips demonstrate that our approach is effective and reliable for crowd detection and semantic scene understanding.