Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Scene invariant crowd counting for real-time surveillance

Automated crowd counting allows excessive crowding to be detected immediately, without the need for constant human surveillance. Current crowd counting systems are location specific, and for these systems to function properly they must be trained on a large amount of data specific to the target location. As such, configuring multiple systems to use is a tedious and time consuming exercise. We propose a scene invariant crowd counting system which can easily be deployed at a different location to where it was trained. This is achieved using a global scaling factor to relate crowd sizes from one scene to another. We demonstrate that a crowd counting system trained at one viewpoint can achieve a correct classification rate of 90% at a different viewpoint.

Crowd counting using group tracking and local features

In public venues, crowd size is a key indicator of crowd safety and stability. In this paper we propose a crowd counting algorithm that uses tracking and local features to count the number of people in each group as represented by a foreground blob segment, so that the total crowd estimate is the sum of the group sizes. Tracking is employed to improve the robustness of the estimate, by analysing the history of each group, including splitting and merging events. A simplified ground truth annotation strategy results in an approach with minimal setup requirements that is highly accurate.

Alone against the crowd: Individual differences in referees' ability to cope under pressure

This paper contributes to the recent debate about the role of referees in the home advantage phenomenon. Specifically, it aims to provide a convincing answer to the newly posed question of the existence of individual differences among referees in terms of the home advantage (Boyko, Boyko, & Boyko, 2007; Johnston, 2008). Using multilevel modelling on a large and representative dataset we find that (1) the home advantage effect differs significantly among referees, and (2) this relationship is moderated by the size of the crowd. These new results suggest that a part of the home advantage is due to the effect of the crowd on the referees, and that some referees are more prone to be influenced by the crowd than others. This provides strong evidence to indicate that referees are a significant contributing factor to the home advantage. The implications of these findings are discussed both in terms of the relevant social psychological research, and with respect to the selection, assessment, and training of referees.

Scene invariant crowd counting

This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Problem-solving courts, therapeutic jurisprudence and the constitution : if two is company, is three a crowd?

Court costs, resource-intensive trials, booming prison populations and the obduracy of recidivism rates all present as ugly excesses of the criminal law adversarial paradigm. To combat these excesses, problem-solving courts have evolved with an edict to address the underlying issues that have caused an individual to commit a crime. When a judge seeks to help a problem-solving court participant deal with issues like addiction, mental health or poverty, they are performing a very different role to that of a judicial officer in the traditional court hierarchy. They are no longer the removed, independent arbiter — a problem-solving court judge steps into the ‘arena’ with the participant and makes active use of their judicial authority to assist in rehabilitation and positive behavioural change. Problem-solving court judges employing the principles of therapeutic jurisprudence appreciate that their interaction with participants can have therapeutic and anti-therapeutic consequences. This article will consider how the deployment of therapeutic measures (albeit with good intention) can lead to the behavioural manifestation of partiality and bias on the part of problem-solving court judges. Chapter III of the Commonwealth Constitution will then be analysed to highlight why the operation and functioning of problem solving courts may be deemed unconstitutional. Part IV of this article will explain how a problem-solving court judge who is not acting impartially or independently will potentially contravene the requirements of the Constitution. It will finally be suggested that judges who possess a high level of emotional intelligence will be the most successful in administering an independent and impartial problem solving court.

How to work the crowd : a snapshot of barriers and motivations to crowdfunding

Introduction This study is a snapshot of Australian donor motivations and donor barriers to crowdfunding, and provides some indicative recommendations on ways the uptake of crowdfunding in the creative industries might increase. It is based upon a literature review and semi-structured interviews with 17 stakeholders who have used crowdfunding in Australia, including: creative producers seeking funds; financial crowdfunding donors; Artsupport Australia mentors of artists who are using crowdfunding; and crowdfunding site stakeholders. About the report Artsupport Australia commissioned the Queensland University of Technology Creative Industries team to produce a report on trends related to crowdfunding, particularly identifying barriers and motivations that might be associated with it. Artsupport Australia suggested a list of interview candidates, based on those individuals’ knowledge or experience with crowdfunding, to provide a better understanding of perceptions of this emerging practice, and to inform discussions on whether it is a useful revenue generating mechanism for the cultural sector.

Cross-dressing footballer tackles issues and scores with the crowd

MISOGYNY, no matter how it's defined, has become a political football. Fifteen minutes into the first quarter of David Williamson's Managing Carmen and Brent Lyall, a star AFL footballer, has already taken a high tackle from his hottie girlfriend, had a clanger with his manager and been caught wearing women's clothes by his acting coach. Not a good look for Brent's sponsors. By full time, Williamson's witty banter and colourful characters have hit their mark. No wonder he's already working on the screenplay.

Integrating crowd-behavior modeling into military simulation using game technology

Crowds of noncombatants play a large and increasingly recognized role in modern military operations and often create substantial difficulties for the combatant forces involved. However, realistic models of crowds are essentially absent from current military simulations. To address this problem, the authors are developing a crowd simulation capable of generating crowds of noncombatant civilians that exhibit a variety of realistic individual and group behaviors at differing levels of fidelity. The crowd simulation is interoperable with existing military simulations using a standard, distributed simulation architecture. Commercial game technology is used in the crowd simulation to model both urban terrain and the physical behaviors of the human characters that make up the crowd. The objective of this article is to present the design and development process of a simulation that integrates commercially available game technology with current military simulations to generate realistic and believable crowd behavior.

Crowd modelling for military simulations using game technology

Crowds of non-combatants play a large and increasingly recognized role in modern military operations, and often create substantial difficulties for the combatant forces involved. However, realistic models of crowds are essentially absent from current military simulations. To address this problem we are developing a crowd simulation capable of generating crowds of non-combatant civilians that exhibit a variety of realistic individual and group behaviours at differing levels of fidelity. The crowd simulation is interoperable with existing military simulations using a standard distributed simulation architecture. Commercial game technology is utilized in the crowd simulation to model both urban terrain and the physical behaviours of the human characters that make up the crowd. The objective of this paper is to present the process involved with the design and development of a simulation that integrates commercially available game technology with current military simulations in order to generate realistic and believable crowd behaviour.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

2 Hour game jam at SLQ Garage Gamer - Live performance with crowd participation

The 2 hour game jam was performed as part of the State Library of Queensland 'Garage Gamer' series of events, summer 2013, at the SLQ exhibition. An aspect of the exhibition was the series of 'Level Up' game nights. We hosted the first of these - under the auspices of brIGDA, Game On. It was a party - but the focal point of the event was a live streamed 2 hour game jam. Game jams have become popular amongst the game development and design community in recent years, particularly with the growth of the Global Game Jam, a yearly event which brings thousands of game makers together across different sites in different countries. Other established jams take place on-line, for example the Ludum Dare challenge which as been running since 2002. Other challenges follow the same model in more intimate circumstances and it is now common to find institutions and groups holding their own small local game making jams. There are variations around the format, some jams are more competitive than others for example, but a common aspect is the creation of an intense creative crucible centred around team work and ‘accelerated game development’. Works (games) produced during these intense events often display more experimental qualities than those undertaken as commercial projects. In part this is because the typical jam is started with a conceptual design brief, perhaps a single word, or in the case of the specific game jam described in this paper, three words. Teams have to envision the challenge key word/s as a game design using whatever skills and technologies they can and produce a finished working game in the time given. Game jams thus provide design researchers with extraordinary fodder and recent years have also seen a number of projects which seek to illuminate the design process as seen in these events. For example, Gaydos, Harris and Martinez discuss the opportunity of the jam to expose students to principles of design process and design spaces (2011). Rouse muses on the game jam ‘as radical practice’ and a ‘corrective to game creation as it is normally practiced’. His observations about his own experience in a jam emphasise the same artistic endeavour forefronted earlier, where the experience is about creation that is divorced from the instrumental motivations of commercial game design (Rouse 2011) and where the focus is on process over product. Other participants remark on the social milieu of the event as a critical factor and the collaborative opportunity as a rich site to engage participants in design processes (Shin et al, 2012). Shin et al are particularly interested in the notion of the site of the process and the ramifications of participants being in the same location. They applaud the more localized event where there is an emphasis on local participation and collaboration. For other commentators, it is specifically the social experience in the place of the jam is the most important aspect (See Keogh 2011), not the material site but rather the physical embodied experience of ‘being there’ and being part of the event. Participants talk about game jams they have attended in a similar manner to those observations made by Dourish where the experience is layered on top of the physical space of the event (Dourish 2006). It is as if the event has taken on qualities of place where we find echoes of Tuan’s description of a particular site having an aura of history that makes it a very different place, redolent and evocative (Tuan 1977). The 2 hour game jam held during the SLQ Garage Gamer program was all about social experience.

Crowd monitoring using computer vision

Novel computer vision techniques have been developed for automatic monitoring of crowed environments such as airports, railway stations and shopping malls. Using video feeds from multiple cameras, the techniques enable crowd counting, crowd flow monitoring, queue monitoring and abnormal event detection. The outcome of the research is useful for surveillance applications and for obtaining operational metrics to improve business efficiency.