Video driven traffic modelling in ParamicsTM

With urbanization and vehicle availability, there exist many traffic problems including congestion, environmental impact and safety. In order to address these problems, we propose a video driven traffic modelling system in this paper. The system can simulate real-world traffic activities in a computer, based on traffic data recorded in videos. Video processing is employed to estimate metrics such as traffic volumes. These metrics are used to update the traffic system model, which is then simulated using the Paramics™ traffic simulation platform. Video driven traffic modelling has widespread potential application in traffic systems, due to the convenience and reduced costs of model development and maintenance. Experiments are conducted in this paper to demonstrate the effectiveness of the proposed system.

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

Combining NL processing and video data to query American football

We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain, and a recognition system which uses the model to recognise American Football plays.

A scalable and extensible segment-event-object-based sports video retrieval system

Sport video data is growing rapidly as a result of the maturing digital technologies that support digital video capture, faster data processing, and large storage. However, (1) semi-automatic content extraction and annotation, (2) scalable indexing model, and (3) effective retrieval and browsing, still pose the most challenging problems for maximizing the usage of large video databases. This article will present the findings from a comprehensive work that proposes a scalable and extensible sports video retrieval system with two major contributions in the area of sports video indexing and retrieval. The first contribution is a new sports video indexing model that utilizes semi-schema-based indexing scheme on top of an Object-Relationship approach. This indexing model is scalable and extensible as it enables gradual index construction which is supported by ongoing development of future content extraction algorithms. The second contribution is a set of novel queries which are based on XQuery to generate dynamic and user-oriented summaries and event structures. The proposed sports video retrieval system has been fully implemented and populated with soccer, tennis, swimming, and diving video. The system has been evaluated against 20 users to demonstrate and confirm its feasibility and benefits. The experimental sports genres were specifically selected to represent the four main categories of sports domain: period-, set-point-, time (race)-, and performance-based sports. Thus, the proposed system should be generic and robust for all types of sports.

New enhancements to cut, fade, and dissolve detection processes in video segmentation

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

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.

Hierarchical recognition of intentional human gestures for sports video annotation

We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.

Incorporating domain knowledge with video and voice data analysis in news broadcasts

This paper addresses the area of video annotation, indexing and retrieval, and shows how a set of tools can be employed, along with domain knowledge, to detect narrative structure in broadcast news. The initial structure is detected using low-level audio visual processing in conjunction with domain knowledge. Higher level processing may then utilize the initial structure detected to direct processing to improve and extend the initial classification.

The structure detected breaks a news broadcast into segments, each of which contains a single topic of discussion. Further the segments are labeled as a) anchor person or reporter, b) footage with a voice over or c) sound bite. This labeling may be used to provide a summary, for example by presenting a thumbnail for each reporter present in a section of the video. The inclusion of domain knowledge in computation allows more directed application of high level processing, giving much greater efficiency of effort expended. This allows valid deductions to be made about structure and semantics of the contents of a news video stream, as demonstrated by our experiments on CNN news broadcasts.

The more things change, the more they stay the same : the 2012 alternative film/video festival

The Alternative Film/Video Festival in Belgrade has historically been one of a triumvirate of critical festivals, with Pula’s MAFAF (1965-1990) and Zagreb’s initiating GEFF (1963-70), servicing experimental, exploratory, avant-garde, personal film in the former Yugoslavia, at Belgrade’s Academic Film Center (AFC) within the Student City Cultural Centre (DKSG). Initiated in 1982 it was resurrected in 2003 with a dual regional and international focus after a hiatus due to the collapse of the socialist states of the former Yugoslavia. As well as a series of curated and retrospective programs each competition program is now split into international and regional halves, selected by Greg de Cuir and Zoran Saveski with production support by Milan Milosavljević. Two film workshops were also available. One on scratch film by Ivan Ladislav Galeta, the other on filming and processing led by Vassily Bourakis. Initiated by de Cuir the first Alternative Film/Video Research Forum was part of the festival this year bringing together research on alternative/ experimental/ avant-garde/ underground film and video. Although I participated in this side-bar I will concentrate here more on discussions from the festival roundtable and contextualise a small number of films, a couple from competition but mainly regional work that I would find difficult to encounter without attendance here.

Freehand 3D Scanning in a Mobile Environment using Video

This work is motivated by two important trends in consumer computing: (i) the growing pervasiveness of mobile computing devices, and (ii) the users’ desire for increasingly complex but readily acquired and manipulated information content. Specifically, we develop and describe a system for 3D model creation of an object, using only a standard mobile device such as a smart phone. Our approach applies the structured light projection methodology and exploits multiple image input such as frames from a video sequence. In comparison with previous work, a significant further challenge addressed here is that of lower quality input data and limited hardware (processing power and memory, camera and projector quality). Novelties include: (i) a comparison of projection pattern detection approaches in the context of a mobile environment – a robust method combining colour detection and a phase congruency descriptor is evaluated, (ii) a model for single view reconstruction which exploits epipolar, coplanarity and topological constraints, (iii) the use of mobile device sensor data in the iterative closest point algorithm used to register multiple partial 3D reconstructions, and (iv) two heuristics for determining the order in which buffered single view based reconstructions are merged. Our experiments demonstrate that visually appealing results are obtained in a speedy manner which does not require specialist knowledge or expertise from the user.

Effects of low- versus high-fidelity simulations on the cognitive burden and performance of entry-level paramedicine students: a mixed-methods comparison trial using eye-tracking, continuous heart rate, difficulty rating scales, video observation and inte

INTRODUCTION: High-fidelity simulation-based training is often avoided for early-stage students because of the assumption that while practicing newly learned skills, they are ill suited to processing multiple demands, which can lead to "cognitive overload" and poorer learning outcomes. We tested this assumption using a mixed-methods experimental design manipulating psychological immersion. METHODS: Thirty-nine randomly assigned first-year paramedicine students completed low- or high-environmental fidelity simulations [low-environmental fidelity simulations (LFenS) vs. high-environmental fidelity simulation (HFenS)] involving a manikin with obstructed airway (SimMan3G). Psychological immersion and cognitive burden were determined via continuous heart rate, eye tracking, self-report questionnaire (National Aeronautics and Space Administration Task Load Index), independent observation, and postsimulation interviews. Performance was assessed by successful location of obstruction and time-to-termination. RESULTS: Eye tracking confirmed that students attended to multiple, concurrent stimuli in HFenS and interviews consistently suggested that they experienced greater psychological immersion and cognitive burden than their LFenS counterparts. This was confirmed by significantly higher mean heart rate (P < 0.001) and National Aeronautics and Space Administration Task Load Index mental demand (P < 0.05). Although group allocation did not influence the proportion of students who ultimately revived the patient (58% vs. 30%, P < 0.10), the HFenS students did so significantly more quickly (P < 0.01). The LFenS students had low immersion resulting in greater assessment anxiety. CONCLUSIONS: High-environmental fidelity simulation engendered immersion and a sense of urgency in students, whereas LFenS created assessment anxiety and slower performance. We conclude that once early-stage students have learned the basics of a clinical skill, throwing them in the "deep end" of high-fidelity simulation creates significant additional cognitive burden but this has considerable educational merit.

Wireless video-based sensor networks for surveillance of residential districts

Compared to traditional wired video sensor networks to supervise a residential district, Wireless Video-based Sensor Networks (WVSN) can provide more detail and precise information while reduce the cost. However, state-of-the-art low cost wireless video-based sensors have very constrained resources such as low bandwidth, small storage, limited processing capability, and limited energy resource. Also, due to the special sensing range of video-based sensors, cluster-based routing is not as effective as it apply to traditional sensor networks. This paper provides a novel real-time change mining algorithm based on an extracted profile model of moving objects learnt from frog's eyes. Example analysis shows the extracted profile would not miss any important semantic images to send to the Base Station for further hazards detection, while efficiently reducing futile video stream data to the degree that nowadays wireless video sensor can realize. Thus it makes WVSN available to surveillance of residential districts.