Video Activism and the Ambiguities of Counter-Surveillance

This paper examines the use of visual technologies by political activists in protest situations to monitor police conduct. Using interview data with Australian video activists, this paper seeks to understand the motivations, techniques and outcomes of video activism, and its relationship to counter-surveillance and police accountability. Our data also indicated that there have been significant transformations in the organization and deployment of counter-surveillance methods since 2000, when there were large-scale protests against the World Economic Forum meeting in Melbourne accompanied by a coordinated campaign that sought to document police misconduct. The paper identifies and examines two inter-related aspects of this: the act of filming and the process of dissemination of this footage. It is noted that technological changes over the last decade have led to a proliferation of visual recording technologies, particularly mobile phone cameras, which have stimulated a corresponding proliferation of images. Analogous innovations in internet communications have stimulated a coterminous proliferation of potential outlets for images Video footage provides activists with a valuable tool for safety and publicity. Nevertheless, we argue, video activism can have unintended consequences, including exposure to legal risks and the amplification of official surveillance. Activists are also often unable to control the political effects of their footage or the purposes to which it is used. We conclude by assessing the impact that transformations in both protest organization and media technologies might have for counter-surveillance techniques based on visual surveillance.

Autism in the air: using Point of View Video Priming and Natural Environment Teaching to help children with autism travel by plane

This paper describes a study that used video materials and visits to an airport to prepare children on the autism spectrum for travel by plane. Twenty parents and carers took part in the study with children aged from 3 to 16 years. The authors explain that the methods they used were based on Applied Behaviour Analysis (ABA) research; a video modeling technique called Point-Of-View Video-priming and during visits to an airport they used procedures known as Natural Environment Teaching. The findings suggest that using video and preparing children by taking them through what is likely to happen in the real environment when they travel by plane is effective and the authors suggest these strategies could be used to support children with autism with other experiences they need or would like to engage in such as visits to the dentist or hairdressers and access to leisure centres and other public spaces.

Energy-Aware Rate and Description Allocation Optimized Video Streaming for Mobile D2D Communications

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

Consulting pupils in Assessment for Learning classrooms: the twists and turns of working with students as co-researchers

This is an invited paper to a special issue on pupil voice focusing on methodological issues arising from the ESRC/TLRP project on consulting pupils about assessment practices in their classrooms. The issue of consulting pupils about assessment has rarely been researched before but what this article illustrates are some of the difficulties, tensions and positive outcomes of engaging with students as researchers within a nationally funded (and therefore externally driven), university-based project. This study adds considerably to the body of knowledge in this area by engaging students in the process as researchers in different capacities within the project. Issues discussed include the use of student advisory groups, ethical negotiation, students undertaking videotaped classroom observations and their subsequent role in co-interpreting video excerpts and visual images. The paper has attracted considerable interest already through the ESRC pupil seminar series forum and also from a prior paper presentation to the European Educational Research Association in September 2006 in Switzerland to the Childrens' Rights SIG becasue of researchers' current interests in embedding democratic principles and practices within research with children and young people.

Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Behaviours in video

With a significant increment of the number of digital cameras used for various purposes, there is a demanding call for advanced video analysis techniques that can be used to systematically interpret and understand the semantics of video contents, which have been recorded in security surveillance, intelligent transportation, health care, video retrieving and summarization. Understanding and interpreting human behaviours based on video analysis have observed competitive challenges due to non-rigid human motion, self and mutual occlusions, and changes of lighting conditions. To solve these problems, advanced image and signal processing technologies such as neural network, fuzzy logic, probabilistic estimation theory and statistical learning have been overwhelmingly investigated.

Diagnostic accuracy and clinical management by realtime teledermatology. Results from the Northern Ireland arms of the UK Multicentre Teledermatology Trial

Diagnostic accuracy and management recommendations of realtime teledermatology consultations using low-cost telemedicine equipment were evaluated. Patients were seen by a dermatologist over a video-link and a diagnosis and treatment plan were recorded. This was followed by a face-to-face consultation on the same day to confirm the earlier diagnosis and management plan. A total of 351 patients with 427 diagnoses participated. Sixty-seven per cent of the diagnoses made over the video-link agreed with the face-to-face diagnosis. Clinical management plans were recorded for 214 patients with 252 diagnoses. For this cohort, 44% of the patients were seen by the same dermatologist at both consultations, while 56% were seen by a different dermatologist. In 64% of cases the same management plan was recommended at both consultations; a sub-optimum treatment plan was recommended in 8% of cases; and in 9% of cases the video-link management plans were judged to be inappropriate. In 20% of cases the dermatologist was unable to recommend a suitable management plan by video-link. There were significant differences in the ability to recommend an optimum management plan by video-link when a different dermatologist made the reference management plan. The results indicate that a high proportion of dermatological conditions can be successfully managed by realtime teledermatology.

Robust Audio-Visual Speech Recognition under Noisy Audio-Video Conditions

This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

A Cross-Layer Optimized Scheme and Its Application in Mobile Multimedia Networks with QoS Provision

To cope with the rapid growth of multimedia applications that requires dynamic levels of quality of service (QoS), cross-layer (CL) design, where multiple protocol layers are jointly combined, has been considered to provide diverse QoS provisions for mobile multimedia networks. However, there is a lack of a general mathematical framework to model such CL scheme in wireless networks with different types of multimedia classes. In this paper, to overcome this shortcoming, we therefore propose a novel CL design for integrated real-time/non-real-time traffic with strict preemptive priority via a finite-state Markov chain. The main strategy of the CL scheme is to design a Markov model by explicitly including adaptive modulation and coding at the physical layer, queuing at the data link layer, and the bursty nature of multimedia traffic classes at the application layer. Utilizing this Markov model, several important performance metrics in terms of packet loss rate, delay, and throughput are examined. In addition, our proposed framework is exploited in various multimedia applications, for example, the end-to-end real-time video streaming and CL optimization, which require the priority-based QoS adaptation for different applications. More importantly, the CL framework reveals important guidelines as to optimize the network performance

Video Event Recognition by Dempster-Shafer Theory

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

A programmable architecture for layered multimedia streams in IPv6 networks

A new configurable architecture is presented that offers multiple levels of video playback by accommodating variable levels of network utilization and bandwidth. By utilizing scalable MPEG-4 encoding at the network edge and using specific video delivery protocols, media streaming components are merged to fully optimize video playback for IPv6 networks, thus improving QoS. This is achieved by introducing “programmable network functionality” (PNF) which splits layered video transmission and distributes it evenly over available bandwidth, reducing packet loss and delay caused by out-of-profile DiffServ classes. An FPGA design is given which gives improved performance, e.g. link utilization, end-to-end delay, and that during congestion, improves on-time delivery of video frames by up to 80% when compared to current “static” DiffServ.

Patient satisfaction with realtime teledermatology in Northern Ireland

Teledermatology consultations were organized between two health centers and two hospitals in Northern Ireland using low-cost videoconferencing equipment. A prospective study of patient satisfaction was carried out. Following each teleconsultation, patients were asked to complete a questionnaire assessing their satisfaction with the service. Over 22 months, 334 patients were seen by a dermatologist over the video-link, and 292 patients (87%) completed the 16-item questionnaire. Patients reported universal satisfaction with the technical aspects of teledermatology. The quality of both the audio and the display was highly acceptable to patients. Personal experiences of the teledermatology consultation were also favourable: 85% felt comfortable using the video-link. The benefits of teledermatology were generally recognized: 88% of patients thought that a teleconsultation could save time. Patients found the teledermatology consultation to be as acceptable as the conventional dermatology consultation. These findings suggest overall patient satisfaction with realtime teledermatology.

Hardware Acceleration of Background Modeling in the Compressed Domain

In intelligent video surveillance systems, scalability (of the number of simultaneous video streams) is important. Two key factors which hinder scalability are the time spent in decompressing the input video streams, and the limited computational power of the processor. This paper demonstrates how a combination of algorithmic and hardware techniques can overcome these limitations, and significantly increase the number of simultaneous streams. The techniques used are processing in the compressed domain, and exploitation of the multicore and vector processing capability of modern processors. The paper presents a system which performs background modeling, using a Mixture of Gaussians approach. This is an important first step in the segmentation of moving targets. The paper explores the effects of reducing the number of coefficients in the compressed domain, in terms of throughput speed and quality of the background modeling. The speedups achieved by exploiting compressed domain processing, multicore and vector processing are explored individually. Experiments show that a combination of all these techniques can give a speedup of 170 times on a single CPU compared to a purely serial, spatial domain implementation, with a slight gain in quality.