Jill Meagher CCTV gothic tendencies in narratives of violence and gender justice

This article initiates an encounter, for feminist media studies, with some of theevidentiary artefacts appropriated for public consumption from the Jill Meagher rape andmurder case. While Jill Meagher was abducted, raped and killed on September 22, 2012, in Melbourne, Australia, the story of her violent death traverses time and place.

The footage is decisive’: Applying the thinking of Marshall McLuhan to CCTV and police misconduct

This article adapts Marshall McLuhan’s writings on mass media to ubiquitous and universal surveillance systems, looking at surveillance as media. The term ‘broadcast media’ is derived from an agricultural metaphor, a technique of planting. I argue that CCTV systems are an inversion of broadcasting: ‘harvest media’. Drawing on three case studies in which CCTV has been relevant to allegations of police misconduct, I explore how harvest media impacts on cultural and legal perceptions of evidence, and what can be known.

CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).

Abnormal behavior detection for early warning of terrorist attack

Many terrorist attacks are accomplished by bringing explosive devices hidden in ordinary-looking objects to public places. In such case, it is almost impossible to distinguish a terrorist from ordinary people just from the isolated appearance. However, valuable clues might be discovered through analyzing a series of actions of the same person. Abnormal behaviors of object fetching, deposit, or exchange in public places might indicate potential attacks. Based on the widely equipped CCTV surveillance systems at the entrance of many public places, this paper proposes an algorithm to detect such abnormal behaviors for early warning of terrorist attack.

Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes

Stormwater pipe systems in Australia are designed to convey water from rainfall and surface runoff only and do not transport sewage. Any blockage can cause flooding events with the probability of subsequent property damage. Proactive maintenance plans that can enhance their serviceability need to be developed based on a sound deterioration model. This paper uses a neural network (NN) approach to model deterioration in serviceability of concrete stormwater pipes, which make up the bulk of the stormwater network in Australia. System condition data was collected using CCTV images. The outcomes of model are the identification of the significant factors influencing the serviceability deterioration and the forecasting of the change of serviceability condition over time for individual pipes based on the pipe attributes. The proposed method is validated and compared with multiple discriminant analysis, a traditionally statistical method. The results show that the NN model can be applied to forecasting serviceability deterioration. However, further improvements in data collection and condition grading schemes should be carried out to increase the prediction accuracy of the NN model.

The effect of community interventions on alcohol-related assault in Geelong, Australia

Alcohol has consistently been demonstrated to increase levels of aggression and violence, particularly in late night licensed venues. Since 2005, the City of Geelong in Australia has implemented a substantial number of interventions to reduce alcohol related violence, including a liquor accord, increased police surveillance, ID scanners, CCTV, a radio network and an alcohol industry sponsored social marketing campaign. The aim of the current study is to assess the individual and collective impact of community interventions on indicators of alcohol-related assaults in the Geelong region. This paper reports stage one findings from the Dealing with Alcohol-related problems in the Night-time Economy project (DANTE) and specifically examines assault rate data from both emergency department presentations, ICD-10 classification codes, and police records of assaults. None of the interventions were associated with reductions in alcohol-related as-sault or intoxication in Geelong, either individually or when combined. However, the alcohol industry sponsored social marketing campaign ‘Just Think’ was associated with an increase in assault rates. Community level interventions appeared to have had little effect on assault rates during high alcohol times. It is also possible that social marketing campaigns without practical strategies are associated with increased assault rates. The findings also raise questions about whether interventions should be targeted at reducing whole-of-community alcohol consumption.

Unlawful acts, unkind architecture and unhelpful perceptions: a case study of Market Square Mall, Geelong

On a quiet Sunday afternoon in January 2015, a 12 year old girl was assaulted in Geelong’s Market Square mall. The attack sparked a media furore over what should be done to address the ongoing safety and amenity issues of this intractable public space. The city’s mayor, Cr Darryn Lyons, responded to the situation by declaring the mall a ‘haunt for bogans and scumbags’ and renewed calls for its demolition. Such rhetoric highlights the exclusionary mindset that casts certain types of people as undesirable inhabitants of public spaces. It also bolsters negative public perceptions of the mall. Once formed, such attitudes are difficult to shift, despite an overall improvement in the area’s crime rates over recent years. Poor perceptions are further reinforced by the soulless nature of the mall’s built fabric and weak urban interfaces. Its formal language is one of hostility, not only towards would-be delinquents, but to all people. The space is furnished with cold stainless steel seats, CCTV cameras and limp, pavement water spouts, while its inactive edges consist of loading bays, security grilles and neglected facades. This paper considers how a more inclusive architecture might be utilised to create a kinder, more generous physical environment that reflects the public nature of the space. While not a quick fix, fostering an architecture that encourages a more equitable use of the mall may diminish the sense of fear, anxiety and suspicion that the space currently elicits, tackling the problem at both a structural and social level.

The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large 'real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited.