Explicit contour model for vehicle tracking with automatic hypothesis validation

This paper addresses the problem of vehicle tracking under a single static, uncalibrated camera without any constraints on the scene or on the motion direction of vehicles. We introduce an explicit contour model, which not only provides a good approximation to the contours of all classes of vehicles but also embeds the contour dynamics in its parameterized template. We integrate the model into a Bayesian framework with multiple cues for vehicle tracking, and evaluate the correctness of a target hypothesis, with the information implied by the shape, by monitoring any conflicts within the hypothesis of every single target as well as between the hypotheses of all targets. We evaluated the proposed method using some real sequences, and demonstrated its effectiveness in tracking vehicles, which have their shape changed significantly while moving on curly, uphills roads. © 2005 IEEE.published_or_final_versionThe IEEE International Conference on Image Processing (ICIP 2005), Genoa, Italy, 11-14 September 2005. In Proceedings of IEEE-ICIP, 2005, v. 2, p. 582-58

Clustering techniques for human posture recognition: K-Means, FCM and SOM

An automated surveillance system should have the ability to recognize human behaviour and to warn security personnel of any impending suspicious activity. Human posture is one of the key aspects of analyzing human behaviour. We investigated three clustering techniques to recognize human posture. The system is first trained to recognize a pair of posture and this is repeated for three pairs of human posture. Finally the system is trained to recognize five postures together. The clustering techniques used for the purpose of our investigation included K-Means, fuzzy C-Means and Self-Organizing Maps. The results showed that K-Means and Fuzzy C-Means performed well for the three pair of posture data. However these clustering techniques gave low accuracy when we scale up the dataset to five different postures. Self- Organizing Maps produce better recognition accuracy when tested for five postures

Fast Frame-Based Image Deconvolution Using Variable Splitting and Constrained Optimization

We propose a new fast algorithm for solving one of the standard formulations of frame-based image deconvolution: an unconstrained optimization problem, involving an $\ell_2$ data-fidelity term and a non-smooth regularizer. Our approach is based on using variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The resulting algorithm efficiently uses a regularized version of the Hessian of the data fidelity term, thus exploits second order information. Experiments on a set of image deblurring benchmark problems show that our algorithm is clearly faster than previous state-of-the-art methods.Comment: Submitted to the 2009 IEEE Workshop on Statistical Signal Processin

A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

Fast Image Recovery Using Variable Splitting and Constrained Optimization

We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an $\ell_2$ data-fidelity term and a non-smooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called "alternating direction method of multipliers", for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.Comment: Submitted; 11 pages, 7 figures, 6 table

Interactive visualization of video content and associated description for semantic annotation

In this paper, we present an intuitive graphic fra- mework introduced for the effective visualization of video content and associated audio-visual description, with the aim to facilitate a quick understanding and annotation of the semantic content of a video sequence. The basic idea consists in the visualization of a 2D feature space in which the shots of the considered video sequence are located. Moreover, the temporal position and the specific content of each shot can be displayed and analysed in more detail. The selected fea- tures are decided by the user, and can be updated during the navigation session. In the main window, shots of the consi- dered video sequence are displayed in a Cartesian plane, and the proposed environment offers various functionalities for automatically and semi-automatically finding and annotating the shot clusters in such feature space. With this tool the user can therefore explore graphically how the basic segments of a video sequence are distributed in the feature space, and can recognize and annotate the significant clusters and their structure. The experimental results show that browsing and annotating documents with the aid of the proposed visuali- zation paradigms is easy and quick, since the user has a fast and intuitive access to the audio-video content, even if he or she has not seen the document yet

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

A robust content-dependent algorithm for video watermarking

ABSTRACT A watermarking method that relies on informed coding and informed embedding is presented. Our method uses a subset of various codewords to represent the 0 and 1 message bits to be embedded. We propose a codeword generation scheme that keeps control of the distance between codewords in order to secure fidelity and robustness of the watermark. When compared to existing video watermarking schemes, our method yields superior robustness to video compression

Iterative algorithms based on decoupling of deblurring and denoising for image restoration

In this paper, we propose iterative algorithms for solving image restoration problems. The iterative algorithms are based on decoupling of deblurring and denoising steps in the restoration process. In the deblurring step, an efficient deblurring method using fast transforms can be employed. In the denoising step, effective methods such as the wavelet shrinkage denoising method or the total variation denoising method can be used. The main advantage of this proposal is that the resulting algorithms can be very efficient and can produce better restored images in visual quality and signalto-noise ratio than those by the restoration methods using the combination of a data-fitting term and a regularization term. The convergence of the proposed algorithms is shown in the paper. Numerical examples are also given to demonstrate the effectiveness of these algorithms. © 2008 Society for Industrial and Applied Mathematics.published_or_final_versio

A depth camera motion analysis framework for tele-rehabilitation : motion capture and person-centric kinematics analysis

With increasing importance given to telerehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standard optical systems, without compromising on accuracy. Novel signal processing and computer vision algorithms are proposed to determine motion patterns of interest from infrared and depth data. In order to demonstrate the proposed framework’s suitability for rehabilitation, we developed a gait analysis application that depends on the underlying motion capture sub-system. Each subject’s individual kinematics parameters, which are unique to that subject, are calculated and these are stored for monitoring individual progress of the clinical therapy. Experiments were conducted on 14 different subjects, 5 healthy and 9 stroke survivors. The results show very close agreement of the resulting relevant joint angles with a 12-camera based VICON system, a mean error of at most 1.75% in detecting gait events w.r.t the manually generated ground-truth, and significant performance improvements in terms of accuracy and execution time compared to a previous Kinect-based system