Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

Shut up and write! Some surprising uses of cafes and crowds in doctoral writing

This chapter discusses a ‘writing movement’, which is currently occurring in various parts of Australia through the support of social media. A concept emerging from the café scene in San Francisco, ‘Shut Up and Write!’ is a meetup group that brings writers together at a specific time and place to write side by side, thus making writing practice, social. This concept has been applied to the academic environment and our case-study explores the positive outcomes in two locations: RMIT University and Queensland University of Technology. This informal learning practice can be implemented to assist research students in developing academic skills.

Public spaces/public disgraces : crowds and the state in contemporary Vietnam

This article argues that a semantic shift in the crowd in Vietnam over the last decade has allowed public space to become a site through which transgressive ideologies and desires may have an outlet. At a time of accelerating social change, the state has effectively delimited public criticism yet a fragile but assertive form of Vietnamese democratic practice has arisen in public space, at the margins of official society, in sites previously equated with state control. Official state functions attract only small audiences, and rather than celebrating the dominance of the party, reveal the disengagement of the populace in the party's activities. Where crowds were always a component of state (stage)-managed events, now public spaces are attracting large numbers of people for supposedly non-political activities which may become transgressive acts condemned by the regime. In support of the notion that crowding is an opening up of the possibility of more subversive political actions, the paper presents an analysis of recent crowd formations and the state's reaction to them. The analysis reveals the modalities through which popular culture has provided the public with the means to transcend the constraints of official, authorized, and legitimate codes of behaviour in public space. Changes in the use of public space, it is argued, map the sets of relations between the public and the state, making these transforming relationships visible, although fraught with contradictions and anomalies.

Large scale monitoring of crowds and building utilisation: A new database and distributed approach

Public buildings and large infrastructure are typically monitored by tens or hundreds of cameras, all capturing different physical spaces and observing different types of interactions and behaviours. However to date, in large part due to limited data availability, crowd monitoring and operational surveillance research has focused on single camera scenarios which are not representative of real-world applications. In this paper we present a new, publicly available database for large scale crowd surveillance. Footage from 12 cameras for a full work day covering the main floor of a busy university campus building, including an internal and external foyer, elevator foyers, and the main external approach are provided; alongside annotation for crowd counting (single or multi-camera) and pedestrian flow analysis for 10 and 6 sites respectively. We describe how this large dataset can be used to perform distributed monitoring of building utilisation, and demonstrate the potential of this dataset to understand and learn the relationship between different areas of a building.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

Explaining effervescence:: Shared social identity and positive experience in crowds

We develop and test a dual pathway model of effervescence - the intensely positive experience of being in a crowd. The model proposes that positive feelings arise when those attending a mass event see each other as sharing a common social identity. This sense of shared identity predicts (a) crowd participants’ ability to enact their valued collective identity, and (b) the intimacy of social relations between crowd members. In turn, both of these are theorized to predict crowd members’ positivity of experience. These ideas are tested using survey data from pilgrims (n = 416) attending the Magh Mela - a month-long Hindu pilgrimage festival in north India. The findings provide clear support for the model.

Crowds: Anonymity for Web Transactions

This is the original Crowds paper from Reiter and Rubin. Please consider it required reading.

Crowds: Anonymity for Web Transactions

This is the original Crowds paper from Reiter and Rubin. Please consider it required reading.

INFO6003 (2013) Crowds: Anonymity for Web Transactions -- Paper

This is the original Crowds paper from Reiter and Rubin. Please consider it required reading.

Discriminating DDoS flows from flash crowds using information distance

Discriminating DDoS flooding attacks from flash crowds poses a tough challenge for the network security community. Because of the vulnerability of the original design of the Internet, attackers can easily mimic the patterns of legitimate network traffic to fly under the radar. The existing fingerprint or feature based algorithms are incapable to detect new attack strategies. In this paper, we aim to differentiate DDoS attack flows from flash crowds. We are motivated by the following fact: the attack flows are generated by the same prebuilt program (attack tools), however, flash crowds come from randomly distributed users all over the Internet. Therefore, the flow similarity among DDoS attack flows is much stronger than that among flash crowds. We employ abstract distance metrics, the Jeffrey distance, the Sibson distance, and the Hellinger distance to measure the similarity among flows to achieve our goal. We compared the three metrics and found that the Sibson distance is the most suitable one for our purpose. We apply our algorithm to the real datasets and the results indicate that the proposed algorithm can differentiate them with an accuracy around 65%.

Distinguishing DDoS attacks from flash crowds using probability metrics

Both Flash crowds and DDoS (Distributed Denial-of-Service) attacks have very similar properties in terms of internet traffic, however Flash crowds are legitimate flows and DDoS attacks are illegitimate flows, and DDoS attacks have been a serious threat to internet security and stability. In this paper we propose a set of novel methods using probability metrics to distinguish DDoS attacks from Flash crowds effectively, and our simulations show that the proposed methods work well. In particular, these mathods can not only distinguish DDoS attacks from Flash crowds clearly, but also can distinguish the anomaly flow being DDoS attacks flow or being Flash crowd flow from Normal network flow effectively. Furthermore, we show our proposed hybrid probability metrics can greatly reduce both false positive and false negative rates in detection.

Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

European Union Trade Policy after Enlargement: Larger Crowds, Shifting Priorities and Informal Decision-Making

The article focuses on the effects of Eastern enlargement on EU trade policy-making. On interest constellation, the article makes a case that protectionist forces have been strengthened relative to liberal forces. This slight protectionist turn is mostly witnessed in the area of anti-dumping and with respect to the Doha trade round. On preference aggregation, guided by a principal–agent framework, it is argued that the growth in the number of actors (principals and interest groups) has not constrained the role of the European Commission (agent). However, it has led to an increase in informal processes and has empowered large trading nations vis-a`-vis smaller and less ‘comitology-experienced’ member states.