ID scanning, the media and the politics of urban surveillance in an Australian regional city

Computerised ID scanning technologies have permeated many urban night-time economies in Australia, the United States, Canada and the United Kingdom. This paper documents how one media organisation’s overt and tacit approval of ID scanners helped to normalise this form of surveillance as a precondition of entry into most licensed venues in the Australian city of Geelong. After outlining how processes of governance “from above” and “from below” interweave to generate distinct political and media demands for strategies to prevent localised crime problems, a chronological reconstruction of media reports over a three-and-a half year period demonstrates how ID scanning became the centrepiece of a holistic reform strategy to combat alcohol-related violence in this nightclub precinct. Several discursive techniques helped to normalise this “technological fix”, while suppressing critical discussion of viable concerns over information privacy, data security and system networking. These
included pairing reports of an initial “signal crime” with examples of “virtual victimhood” to stress the urgency of a radical surveillance-based response, which was supported by anecdotal statements from key “primary definers” highlighting the success of this initiative in targeting a wider population of antisocial “others”. The implications of these reporting practices are discussed in light of the media’s central role in reforming the Geelong night-time economy and broader trends in using novel surveillance technologies to combat urban crime problems at the expense of alternative measures that protect individual liberty.

Multiple camera coordination in a surveillance system

We present a distributed surveillance system that uses multiple cheap static cameras to track multiple people in indoor environments. The system has a set of Camera Processing Modules and a Central Module to coordinate the tracking tasks among the cameras. Since each object in the scene can be tracked by a number of cameras, the problem is how to choose the most appropriate camera for each object. This is important given the need to deal with limited resources (CPU, power etc.). We propose a novel algorithm to allocate objects to cameras using the object-to-camera distance while taking into account occlusion. The algorithm attempts to assign objects in the overlapping field of views to the nearest camera, which can see the object without occlusion. Experimental results show that the system can coordinate cameras to track people and can deal well with occlusion.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three detectors for detecting abnormal activity are generated using negative selection. Tracks gathered by people’s movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

Comparative evaluation of pedestrian detection methods for mobile bus surveillance

We present a comparative evaluation of the state-of-art algorithms for detecting pedestrians in low frame rate and low resolution footage acquired by mobile sensors. Four approaches are compared: a) The Histogram of Oriented Gradient (HoG) approach [1]; b) A new histogram feature that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters [2] corresponding to the quantised orientation, called Histogram of Oriented Gradient Banks (HoGB) approach; c) The codebook based HoG feature with branch-and-bound (efficient subwindow search) algorithm [3] and; d) The codebook based HoGB approach. Results show that the HoG based detector achieves the highest performance in terms of the true positive detection, the HoGB approach has the lowest false positives whilst maintaining a comparable true positive rate to the HoG, and the codebook approaches allow computationally efficient detection.

Pedestrian detection for mobile bus surveillance

In this paper, we present a system for pedestrian detection involving scenes captured by mobile bus surveillance cameras in busy city streets. Our approach integrates scene localization, foreground and background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data. In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarities and second stage further clusters these aligned frames in terms of lighting. This produces clusters of images which are differential in viewpoint and lighting. A kernel density estimation (KDE) method for colour and gradient foreground-background separation are then used to construct background model for each image cluster which is subsequently used to detect all foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be identified. We have tested our system on a set of real bus video datasets and the experimental results verify that our system works well in practice.

Context aware privacy in visual surveillance

In this paper we present preliminary work implementing dynamic privacy in public surveillance. The aim is to maximise the privacy of those under surveillance, while giving an observer access to sufficient information to perform their duties. As these aspects are in conflict, a dynamic approach to privacy is required to balance the system's purpose with the system's privacy. Dynamic privacy is achieved by accounting for the situation, or context, within the environment. The context is determined by a number of visual features that are combined and then used to determine an appropriate level of privacy.

Distributed query processing for mobile surveillance

Addressing core issues in mobile surveillance, we present an architecture for querying and retrieving distributed, semi-permanent multi-modal data through challenged networks with limited connectivity. The system provides a rich set of queries for spatio-temporal querying in a surveillance context, and uses the network availability to provide best quality of service. It incrementally and adaptively refines the query, using data already retrieved that exists on static platforms and on-demand data that it requests from mobile platforms. We demonstrate the system using a real surveillance system on a mobile 20 bus transport network coupled with static bus depot infrastructure. In addition, we show the robustness of the system in handling different conditions in the underlying infrastructure by running simulations on a real, but historic dataset collected in an offline manner.

An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three types of detectors for detecting abnormal activity are developed using negative selection. Tracks gathered by people's movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

Classifying and detecting group behaviour from visual surveillance data

We outline an approach to classifying and detecting behaviours from surveillance data. Simple pairwise movement patterns are learned and used as building blocks to describe behaviour over a temporal sequence, or compared with other pairs to detect group behaviour. As the pair primitives are easy to redefine and learn, and complex behaviour over time is specified by the user as a sequence of pair primitives, this approach provides a flexible yet robust method of detecting complex movement in a wide variety of domains.

Detecting deviations from known paths and speeds in a surveillance situation

This paper presents techniques for analysing human behaviour via video surveillance. In known scenes under surveillance, common paths of movement between entry and exit points are obtained and classified. These are used, together with a priori velocity data, to serve as a model of normal traffic flow in the scene. Surveillance sequences are then processed to extract and track the movement of people in the scene, which is compared with the models to enable detection of abnormal movement

Vascular access surveillance : the role of the access nurse in the renal dialysis unit.

Introduction
In January 2006, the Renal Dialysis Unit at Geelong Hospital appointed a Vascular Access Nurse. A Transonic Flow Qc HDO2 Ultrasound Dilution Monitor was purchased to monitor access flow and recirculation in arteriovenous fistulae in an attempt to predict AVF stenoses requiring early surgical correction.

Methods
A bi-monthly monitoring program tested all facility-based patients. 82 patients were assessed for access flow and recirculation between February and December 2006.

Results
18 (22%) had poor AVF function; 13 with access flows <500ml/minute on initial testing and 5 with an access flow decreasing >25% over a four month period. Of the 18 patients shown to have poor access flow, 2 died within one month of measurement while 5 were too frail to attempt corrective surgery. The remaining 11 proceeded to ultrasound or fistulography. A >50% stenosis was detected in all 11 cases. Of these, 4 had successful vein patch surgery and one had PTFE grafting, each with marked improvement in access flow. One had failed vein patch surgery requiring creation of a femoral AVF, one patient required cvc insertion to await AVF creation, and one had failed stenting requiring a permanent cvc. 3 died before planned surgery.

Conclusion
5 of the 82 patients that had access flow assessment, and needed further evaluation, proceeded to successful pre-emptive surgical intervention. We believe the Transonic is a useful adjunct to routine clinical AVF surveillance, in providing early evidence of AVF failure that can be avoided by pre-emptive surgery.