Crowded Scene Analysis: A Survey

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.Comment: 20 pages in IEEE Transactions on Circuits and Systems for Video Technology, 201

Crowd Behavior Analysis: A Review where Physics meets Biology

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision stud- ies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.Comment: Accepted in Neurocomputing, 31 pages, 180 reference

A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes

This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. These semantic regions can be used to recognize pre-defined activities in crowd scenes. Finally, we introduce a cluster-and-merge process which automatically discovers recurrent activities in crowd scenes by clustering and merging the extracted coherent motions. Experiments on various videos demonstrate the effectiveness of our approach.Comment: This manuscript is the accepted version for TIP (IEEE Transactions on Image Processing), 201

High-frequency crowd insights for public safety and congestion control

We present results from several projects aimed at enabling the real-time understanding of crowds and their behaviour in the built environment. We make use of CCTV video cameras that are ubiquitous throughout the developed and developing world and as such are able to play the role of a reliable sensing mechanism. We outline the novel methods developed for our crowd insights engine, and illustrate examples of its use in different contexts in the urban landscape. Applications of the technology range from maintaining security in public spaces to quantifying the adequacy of public transport level of service

Review on Computer Vision Techniques in Emergency Situation

In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.Comment: 25 page

An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance

In recent years, crowd analysis is important for applications such as smart cities, intelligent transportation system, customer behavior prediction, and visual surveillance. Understanding the characteristics of the individual motion in a crowd can be beneficial for social event detection and abnormal detection, but it has rarely been studied. In this paper, we focus on the extraversion measure of individual motions in crowds based on trajectory data. Extraversion is one of typical personalities that is often observed in human crowd behaviors and it can reflect not only the characteristics of the individual motion, but also the that of the holistic crowd motions. To our best knowledge, this is the first attempt to analyze individual extraversion of crowd motions based on trajectories. To accomplish this, we first present a effective composite motion descriptor, which integrates the basic individual motion information and social metrics, to describe the extraversion of each individual in a crowd. The social metrics consider both the neighboring distribution and their interaction pattern. Since our major goal is to learn a universal scoring function that can measure the degrees of extraversion across varied crowd scenes, we incorporate and adapt the active learning technique to the relative attribute approach. Specifically, we assume the social groups in any crowds contain individuals with the similar degree of extraversion. Based on such assumption, we significantly reduce the computation cost by clustering and ranking the trajectories actively. Finally, we demonstrate the performance of our proposed method by measuring the degree of extraversion for real individual trajectories in crowds and analyzing crowd scenes from a real-world dataset.Comment: require modificatio

Energy-based Models for Video Anomaly Detection

Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection. However, building an effective anomaly detection system is a non-trivial task since it requires to tackle challenging issues of the shortage of annotated data, inability of defining anomaly objects explicitly and the expensive cost of feature engineering procedure. Unlike existing appoaches which only partially solve these problems, we develop a unique framework to cope the problems above simultaneously. Instead of hanlding with ambiguous definition of anomaly objects, we propose to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice. This allows our system to be trained completely in an unsupervised procedure and liberate us from the need for costly data annotation. By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores). Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that have been dominating anomaly detection research over many decades. We demonstrate our proposal on the specific application of video anomaly detection and the experimental results indicate that our method performs better than baselines and are comparable with state-of-the-art methods in many benchmark video anomaly detection datasets

Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction

This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces. People in the videos are expected to intentionally take shortest paths toward functional objects subject to obstacles, where people can satisfy certain needs (e.g., a vending machine can quench thirst), by following one of three possible intent behaviors: reach a single functional object and stop, or sequentially visit several functional objects, or initially start moving toward one goal but then change the intent to move toward another. Since detecting functional objects in low-resolution surveillance videos is typically unreliable, we call them "dark matter" characterized by the functionality to attract people. We formulate the Agent-based Lagrangian Mechanics wherein human trajectories are probabilistically modeled as motions of agents in many layers of "dark-energy" fields, where each agent can select a particular force field to affect its motions, and thus define the minimum-energy Dijkstra path toward the corresponding source "dark matter". For evaluation, we compiled and annotated a new dataset. The results demonstrate our effectiveness in predicting human intent behaviors and trajectories, and localizing functional objects, as well as discovering distinct functional classes of objects by clustering human motion behavior in the vicinity of functional objects

Understanding People Flow in Transportation Hubs

In this paper, we aim to monitor the flow of people in large public infrastructures. We propose an unsupervised methodology to cluster people flow patterns into the most typical and meaningful configurations. By processing 3D images from a network of depth cameras, we build a descriptor for the flow pattern. We define a data-irregularity measure that assesses how well each descriptor fits a data model. This allows us to rank flow patterns from highly distinctive (outliers) to very common ones. By discarding outliers, we obtain more reliable key configurations (classes). Synthetic experiments show that the proposed method is superior to standard clustering methods. We applied it in an operational scenario during 14 days in the X-ray screening area of an international airport. Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management. Beyond regular flows, our method identifies a set of rare events corresponding to uncommon activities (cleaning, special security and circulating staff).Comment: 10 pages, 19 figure, 1 tabl

A new network-based algorithm for human activity recognition in video

In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages while human activities can be modeled as the process of package transmission in the network. By analyzing these specific "package transmission" processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in the paper. Experimental results demonstrate the effectiveness of our proposed algorithm.Comment: This manuscript is the accepted version for TCSVT (IEEE Transactions on Circuits and Systems for Video Technology