Online updating appearance generative mixture model for meanshift tracking

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

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Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Facial movement analysis in ASL

In the age of speech and voice recognition technologies, sign language recognition is an essential part of ensuring equal access for deaf people. To date, sign language recognition research has mostly ignored facial expressions that arise as part of a natural sign language discourse, even though they carry important grammatical and prosodic information. One reason is that tracking the motion and dynamics of expressions in human faces from video is a hard task, especially with the high number of occlusions from the signers’ hands. This paper presents a 3D deformable model tracking system to address this problem, and applies it to sequences of native signers, taken from the National Center of Sign Language and Gesture Resources (NCSLGR), with a special emphasis on outlier rejection methods to handle occlusions. The experiments conducted in this paper validate the output of the face tracker against expert human annotations of the NCSLGR corpus, demonstrate the promise of the proposed face tracking framework for sign language data, and reveal that the tracking framework picks up properties that ideally complement human annotations for linguistic research.

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Video frame rate up conversion under inconsistent camera motion

In this paper, we address the problem of video frame rate up-conversion (FRC) in the compressed domain. FRC is often recognized as video temporal interpolation. This problem is very challenging when targeted for video sequences with inconsistent camera and object motion, such as sports videos. A novel compressed domain motion compensation scheme is presented and applied in this paper, aiming at up-sampling frame rates in sports videos. MPEG-2 encoded motion vectors (MVs) are utilized as inputs in the proposed algorithm. The decoded MVs undergo a cumulative spatiotemporal interpolation. An iterative rejection scheme based on the dense motion vector field (MVF) and the generalized affine motion model is exploited to detect global camera motion. Subsequently, the foreground object separation is performed by additionally examining the temporal consistency of the output of iterative rejections. This consistency check process helps coalesce the resulting foreground blocks and weed out the unqualified blocks. Finally, different compensation strategies for the camera and object motions are applied to interpolate the new frames. Illustrative examples are provided to demonstrate the efficacy of the proposed approach. Experimental results are compared with the popular block and non-block based frame interpolation approaches.

Real-time predictive zoom tracking for digital still cameras

Zoom tracking is becoming a standard feature in digital still cameras (DSCs). It involves keeping an object of interest in focus during the zooming-in or zooming-out operation. Zoom tracking is normally achieved by moving the focus motor in real-time according to the so-called trace curves in response to changes in the zoom motor position. A trace curve denotes in-focus motor positions versus zoom motor positions for a specific object distance. A zoom tracking approach is characterized by the way these trace curves are estimated and followed. In this paper, a new zoom tracking approach, named predictive zoom tracking (PZT), is introduced based on two prediction models: auto-regressive and recurrent neural network. The performance of this approach is compared with the existing zoom tracking approaches commonly used in DSCs. The real-time implementation results obtained on an actual digital camera platform indicate that the developed PZT approach not only achieves higher tracking accuracies but also effectively addresses the key challenge of zoom tracking, namely the one-to-many mapping problem.

An EyeTap video-based featureless projective motion estimation assisted by gyroscopic tracking for wearable computer mediated reality

In this paper we present a computationally economical method of recovering the projective motion of head mounted cameras or EyeTap devices, for use in wearable computer-mediated reality. The tracking system combines featureless vision and inertial methods in a closed loop system to achieve accurate robust head tracking using inexpensive sensors. The combination of inertial and vision techniques provides the high accuracy visual registration needed for fitting computer graphics onto real images and the robustness to large interframe camera motion due to fast head rotations. Operating on a 1.2 GHz Pentium III wearable computer with graphics accelerated hardware, the system is able to register live video images with less than 2 pixels of error (0.3 degrees) at 12 frames per second. Fast image registration is achieved by offloading computer vision computation onto the graphics hardware, which is readily available on many wearable computer systems. As an application of this tracking approach, we present a system which allows wearable computer users to share views of their current environments that have been stabilised to another viewer's head position.

Improving target detection by coupling it with tracking

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where target detection is integrated with tracking to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other, we feed the results of tracking back to the detection stage in order to adaptively optimize the detection threshold and improve system robustness. First, the initial target locations are extracted using background subtraction. To model the background, we employ Support Vector Regression (SVR) which is updated over time using an on-line learning scheme. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and improve the confidence level of detection. For verification, additional information based on size, color and motion information is utilized. Feeding back the results of tracking to the detection stage restricts the range of detection threshold values, suppresses false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing perspective projection distortions. We have validated the proposed framework in two different application scenarios, one detecting vehicles at a traffic intersection using visible video and the other detecting pedestrians at a university campus walkway using thermal video. Our experimental results and comparisons with frame-based detection and kernel-based tracking methods illustrate the robustness of our approach.

Dynamic space reconfiguration for Bayesian search and tracking with moving targets

This paper presents a technique for dynamically reconfiguring search spaces in order to enable Bayesian autonomous search and tracking missions with moving targets. In particular, marine search and rescue scenarios are considered, highlighting the need for space reconfiguration in situations where moving targets are involved. The proposed technique improves the search space configuration by maintaining the validity of the recursive Bayesian estimation. The advantage of the technique is that autonomous search and tracking can be performed indefinitely, without loss of information. Numerical results first show the effectiveness of the technique with a single search vehicle and a single moving target. The efficacy of the approach for coordinated autonomous search and tracking is shown through simulation, incorporating multiple search vehicles and multiple targets. The examples also highlight the added benefit to human mission planners resulting from the technique’s simplification of the search space allocation task.

Efficient and robust shot change detection

In this article, we deal with the problem of shot change detection which is of primary importance when trying to segment and abstract video sequences. Contrary to recent experiments, our aim is to elaborate a robust but very efficient (real-time even with uncompressed data) method to deal with the remaining problems related to shot change detection: illumination changes, context and data independency, and parameter settings. To do so, we have considered some adaptive threshold and derivative measures in a hue-saturation colour space. We illustrate our robust and efficient method by some experiments on news and football broadcast video sequences.

A Variational Technique for Time Consistent Tracking of Curves and Motion

In this paper, a new framework for the tracking of closed curves and their associated motion fields is described. The proposed method enables a continuous tracking along an image sequence of both a deformable curve and its velocity field. Such an approach is formalized through the minimization of a global spatio-temporal continuous cost functional, w.r.t a set of variables representing the curve and its related motion field. The resulting minimization process relies on optimal control approach and consists in a forward integration of an evolution law followed by a backward integration of an adjoint evolution model. This latter pde includes a term related to the discrepancy between the current estimation of the state variable and discrete noisy measurements of the system. The closed curves are represented through implicit surface modeling, whereas the motion is described either by a vector field or through vorticity and divergence maps depending on the kind of targeted applications. The efficiency of the approach is demonstrated on two types of image sequences showing deformable objects and fluid motions.

Fast Blind Measurement of Blocking Artifacts in both Pixel and DCT Domains

The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video compression. In this paper, we propose a novel measurement algorithm for blocking artifacts. Computer simulation results indicate that the proposed method accurately measures the blocking artifacts without using the original image. Moreover, the proposed algorithm can be easily implemented in both pixel and DCT domains.

Visual measurement and tracking in laser hybrid welding

This paper presents a novel system for the automatic analysis of a hybrid welding process. High-speed imaging and laser illumination are used to measure the regularity of electric arc frequency and flight directions of filler metal droplets. A fuzzy c-means clustering method is used to detect arcs and segment the video sequences. The droplets are localized by combining principal component analysis and a support vector machine classifier. The flight of a droplet is tracked using Kalman filtering. Experiments indicate that the system is able to track the flights of droplets and to determine the regularity of the arc frequency with a high accuracy if the imaging conditions are stable.

A scalable signature scheme for video authentication

This paper addresses the problem of ensuring the integrity of a digital video and presents a scalable signature scheme for video authentication based on cryptographic secret sharing. The proposed method detects spatial cropping and temporal jittering in a video, yet is robust against frame dropping in the streaming video scenario. In our scheme, the authentication signature is compact and independent of the size of the video. Given a video, we identify the key frames based on differential energy between the frames. Considering video frames as shares, we compute the corresponding secret at three hierarchical levels. The master secret is used as digital signature to authenticate the video. The proposed signature scheme is scalable to three hierarchical levels of signature computation based on the needs of different scenarios. We provide extensive experimental results to show the utility of our technique in three different scenarios—streaming video, video identification and face tampering.

Adaptive training of video sets for image recognition on mobile phones

We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera-equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server-system for improving data acquisition and for supporting scale-invariant object recognition. A static as well as a dynamic training technique are presented that preprocess the collected object data differently and apply two types of neural networks (NN) for classification. Furthermore, the system enables a temporal adaptation for ensuring a continuous data acquisition to improve the recognition rate over time. A formal field experiment reveals current recognition rates and indicates the practicability of both methods under realistic conditions in a museum.

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Applying Semantic Techniques to Search and Analyze Bug Tracking Data

The Web has become an important knowledge source for resolving system installation problems and for working around software bugs. In particular, web-based bug tracking systems offer large archives of useful troubleshooting advice. However, searching bug tracking systems can be time consuming since generic search engines do not take advantage of the semi-structured knowledge recorded in bug tracking systems. We present work towards a semantics-based bug search system which tries to take advantage of the semi-structured data found in many widely used bug tracking systems. We present a study of bug tracking systems and we describe how to crawl them in order to extract semi-structured data. We describe a unified data model to store bug tracking data. The model has been derived from the analysis of the most popular systems. Finally, we describe how the crawled data can be fed into a semantic search engine to facilitate semantic search.

Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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Just in Time Control of Constrained (<Emphasis Type="Italic">max</Emphasis>,+)-Linear Systems

This paper deals with just in time control of (max,+)-linear systems. The output tracking problem, considered in previous studies, is generalized by considering additional constraints in the control objective. The problem is formulated as an extremal fixed point computation. This control is applied to timetables computation for urban bus networks.

E-redesigning of society: towards experiential connectivity of generations in Lithuania

The paper reflects on the unique experience of social and technological development in Lithuania since the regaining of independence as a newly reshaped society constructing a distinctive competitive IST-based model at global level. This has presented Lithuanian pattern of how to integrate different experiences and relations between generations in implementing complex information society approaches. The resulting programme in general is linked to the Lisbon objectives of the European Union. The experience of transitional countries in Europe, each different but facing some common problems, may be useful to developing countries in Africa.

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