@ArrowLLL 2017-12-08T13:23:00.000000Z 字数 9474 阅读 1093

# Application of Pedestrian Behavior Analysis and Visual Crowd Analysis

OPTIMAL

## 智能环境设计与规划

Crowd analysis: a survey （from 《Machine Vision and Applications》2008）当中提到人群分析也可以为公共空间的设计提供指导。例如，设计购物商场的布局使其对顾客更加方便，或者最大化一个办公室的空间设计等等。

## 视觉应用设计与仿真

Crowds by Example （from 《Computer Graphics forum》2007） 当中提到，计算机仿真生成的人群在电影、电脑游戏和其他虚拟世界应用于仿真中变得越来越普遍。随着这些方面的应用对高质量现实感和场景复杂性越来越严苛的要求，真实可信的人群仿真需求也变得越来越大。尽管有灵气的单人动作的模拟方法已经取得极高的成就，但创造一个真实可信的人群仍然是一个巨大的挑战。

## 密集环境个体导航

Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes （ECCV 2016）Forecasting Social Navigation in Crowded Complex Scenes （from arxiv.org） 中提到，当行人和骑自行车的人在密集环境如校园、购物商场等空间穿行时，都会基于一些"社会"规则（如靠右行，避开车流等）互相配合尽量减小干扰。这些可以通过建立模型学得这些规则与交互，应用于个体导航。

Planning-based prediction for pedestrians （from IROS 2009. IEEE）中说到，在人流当中确定机器人的运动已经well-studied但仍然很困难。机器人避免碰撞的方法总是很难让人满意，机器人应当学会预测行人的位置然后做出相应的计划避免碰撞。

## Social LSTM: Human Trajectory Prediction in Crowded Spaces

from "CVPR 2016"

• socially-aware robots

For many tasks in populated environments, robots need to keep track of current and future motion states of people.
people tracking is a key technology for mobile robots to be safely and efficiently deployed in populated environments.

• design of intelligent tracking systems

Crowd behavior analysis : automatic detection of riots or chaotic acts in crowds and localization of the abnormal regions in scenes for high resolution analysis

from《Neurocomputing》

• target tracking[4]

Detecting and tracking people in crowded scenes is a crucial component for a wide range of applications including surveillance, group behavior modeling and crowd disaster prevention.

• video anomaly detection5

Video anomaly detection has become an important research aspect in the area of intelligent visual surveillance due to growing security needs. With the application of video surveillance in modern life growing gradually, drawbacks of conventional surveillance are revealing themselves; for example, spending a long time staring at monitors causes operators fatigue and inattention, sometimes causing them to neglect certain underlying dangerous occurrences. Additionally, since existing surveillance functions tend to capture evidence in a surveying manner, they cannot provide a warning when risk events are forming. Although automated anomaly-detection has attractive potential, it is also one of difficult problems in video analysis. Firstly, unusual events are rare, difficult to describe, and often subtle; secondly, visual behavior is diverse and complex in a realistic and unconstrained environment; thirdly, the description and definition of normality and abnormality, have high uncertainty and depend on changing visual contexts.

• crowd counting[7]
Counting crowd flow is a video-frame analyzing process, which uses computer vision techniques to estimate the crowd information. It has been widely applied in fields such as public security, urban public transport, resource allocation, and optimization.

## Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes

• Humans navigate crowded spaces (introduction)

When pedestrians or bicyclists navigate their way through crowded spaces such as a university campus, a shopping mall or the sidewalks of a busy street, they follow common sense conventions based on social etiquette.

• crowd simulation[1-5]

Computer generated crowds are thus becoming common in films, computer games and other virtual world applications and simulations. As these applications continue to strive towards higher levels of realism and scene complexity, there is an increasing need for realistic and believable crowd simulations.

• pedestrian behavior modeling

1. Architects are interested in understanding how individuals move into buildings to create optimal space designs.
2. Transport engineers face the problem of integration of transportation facilities, with particular emphasis on safety issues for pedestrians.
3. Recent tragic events have increased the interest for automatic video surveillance systems, able to monitor pedestrian flows in public spaces, throwing alarms when abnormal behavior occurs.
4. Special emphasis has been given to more specific evacuation scenarios, for obvious reasons.
• determining robot movements

Robots should certainly never collide with people 11, but avoiding collisions alone is often unsatisfactory because the disruption of almost colliding can be burdensome to people and sub-optimal for robots. Instead, robots should predict the future locations of people and plan routes that will avoid such hindrances (i.e., situations where the person's natural behavior is disrupted due to a robot's proximity) while still efficiently achieving the robot's objectives.

## Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints

• Human re-identification
Human re-identification is a fundamental and crucial problem for multi-camera surveillance systems. It involves re-identifying individuals after they leave field-of-view (FOV) of one camera and appear in FOV of another camera.

automatic re-identification in dense crowds will allow successful monitoring and analysis of crowded events.

## Forecasting Social Navigation in Crowded Complex Scenes

• humans navigate a crowed space
When pedestrians walk in a crowded space such as a university campus, a shopping mall or the sidewalks of a busy street, they follow common sense conventions based on social etiquette

## Patch to the Future: Unsupervised Visual Prediction

• visual prediction

We believe the task of visual prediction is important for two main reasons

• (a) For intelligent agents and systems, prediction is vital for decision making. For example, in order to perform assistive activities, robots must be able to predict the intentions of other agents in the scene. Even a task as simple as walking through a crowded hallway requires the prediction of human trajectories.
• (b) More importantly, prediction requires deep understanding of the visual world and complex interplay between different elements of the scene. Therefore, prediction can act as a way to define “what does it mean to understand an image,” and the task of visual prediction can act as the litmus test for scene understanding.

## Forecasting Interactive Dynamics of Pedestrians With Fictitious Play

• interactive autonomous systems
We focus on predictive models since they are important for developing interactive autonomous systems (e.g., autonomous cars, home robots, smart homes) that can understand different human behavior and pre-emptively respond to future human actions.

## Crowd Behavior Analysis via Curl and Divergence of Motion Trajectories

• profiling crowd attributes

crowd density, collectiveness and stability

• behavior understanding

abnormal event detection, crowd scene classification and crowd behavior recognition

## Crowd analysis: a survey

• Crowd management

Crowd analysis can be used for developing crowd management strategies, especially for increasingly more frquent and popular events such as sport matches, large concerts, public demonstrations and so on, to avoid crowd related disasters and insure public safety

• Public space design

Crowd analysis can provide guidelines for the design of public spaces, e.g. to make the layout of shopping malls more convenient to costumers or to optimize the space usage of an office.

• Virtual environments

Mathematical models of crowds can be employed in virtual environments to enhance the simulation of crowd phenomena, to enrich the human life experience.

• Visual surveillance

Crowd analysis can be used for automatic detection of anomalies and alarms. Furthermore, the ability to track individuals in a crowd could help the police to catch suspects.

• Intelligent environments

In some intelligent environments which involve large groups of people, crowd analysis is a prerequisite for assisting the crowd or an individual in the crowd. For example, in a museum deciding how to divert the crowd based on to the patterns of crowd.

## Pedestrian Travel Time Estimation in Crowded Scenes

• Pedestrian Travel Time Estimation
Pedestrian travel time between entrances and exits indicates traffic efficiency and travel cost of a scene, and thus attracts great attention in surveillance applications. When the travel time increases due to scene congestion, security administrators can take prompt actions, such as blocking some entrances until the congested crowds disperse, or opening extra exists, to control traffic.

Travelers can also use such information to make plans.

Travel time itself can be also regarded as an important feature to describe each individual's behavior and determine whether a pedestrian behaves normally or not.

## Globally Consistent Multi-People Tracking using Motion Patterns

• Multiple object tracking
Multiple object tracking (MOT) has a long tradition for
applications such as radar tracking

## Encoding Motion Cues for Pedestrian Path Prediction in Dense Crowd Scenarios

• Crowd management

Crowd modeling and analysis can help comprehending, thus managing, public traffic and gatherings, as well as related events.

• Urban planning

Understanding the undergoing interactions in the crowd as well as the behaviors of individuals in common places can assist in designing the structural layout of public spaces in order to accommodate the different crowd mobility flows.

• Security and risk management

Monitoring public masses for the aim of security and hazard prevention is one of the top priorities with regards to today’s society. The automatization of such process is therefore pivotal to aid ensuring secure and smooth daily activities. Moreover, detecting abnormalities may even help alarming yet preventing potential future threats. In turn, time, cost and human labor can potentially be saved.

## Forecast the Plausible Paths in Crowd Scenes

• navigation of autonomous vehicles
• recognition of potential risks in video surveillance, etc.

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