56 research outputs found

    Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

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    One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images

    Pedestrian Detection at Day/Night Time with Visible and FIR Cameras : A Comparison

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    Altres ajuts: DGT (SPIP2014-01352)Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images; (b) just infrared images; and (c) both of them. In order to obtain results for the last item, we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset that we have built for this purpose as well as on the publicly available KAIST multispectral dataset

    Foot Detection Method for Footwear Augmented Reality Applications

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    Liitreaalsus on populaarsust koguv platvorm rõivaste ning aksessuaaride kasutamise visualiseerimiseks. Ideaalis võimaldab see kasutajatel proovida erinevaid riideid, jalatseid ja aksessuaare, kasutades ainult üht kaamerat ning sobivat rakendust, mis võimaldab kuvada erinevaid valikuid.\n\rJalatsite liitreaalsuses on palju erinevaid lahendusi, et pakkuda kasutajatele liitreaalsuse kogemust. Need lahendused kasutavad erinevaid meetodeid, nagu fikseeritud kaamera, muutumatu taust ja markerid jalgadel tuvastuse hõlbustamiseks. Nende meetodite hulgas pole ükski kindlalt parem, lihtsam või kiirem. Lisaks puudub tihtipeale avalikkusel ligipääs arendatud rakendustele.\n\rKäesolev magistritöö proovis leida universaalset lahendust, mis sobiks kasutamiseks kõigi tulevaste jalatsite liitreaalsuse rakendustega.Augmented reality is gaining popularity as a technique for visualizing apparel usage. Ide-ally it allows users virtually to try out different clothes, shoes, and accessories, with only a camera and suitable application which encompasses different apparel choices.\n\rFocusing on augmented reality for footwear, there is a multitude of different solutions on how to offer the reality augmentation experience to the end users. These solutions employ different methods to deliver the end result, such as using fixed camera and constant back-ground or requiring markers on feet for detection. Among the variety of techniques used to approach the footwear reality augmentation, there is no single best, simplest, or fastest solution. The solutions’ sources aren’t usually even publicly available. \n\rThis thesis tries to come up with a solution for the footwear reality augmentation problem, which can be used as a base for any proceeding footwear augmented reality projects. This intentionally universal approach will be created by researching possible combinations of potential methods that can ensure a solutions regarding footwear reality augmentation. \n\rIn general, the idea behind this thesis work is to conduct a literature review about different techniques and come up with the best and robust algorithm or combination of methods that can be used for footwear augmented reality.\n\rA researched, documented, implemented and publicized solution would allow any upcom-ing footwear augmented reality related project to start working from an established base, therefore reducing time waste on already solved issues and possibly improving the quality of the end result.\n\rThe solution presented in this thesis is developed with focus on augmented reality applica-tions. The method is neither specific to any platform nor does it have heavy location re-quirements. The result is a foot detection algorithm, capable of working on commonly available hardware, which is beneficial for augmented reality application

    Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery

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    Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. Recent work has tackled this challenge head on by returning to classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the bounds of current accuracy norms. A key limitation in such approaches is the assumption of posture – that the observed pedestrian is at full height stance within the scene. Whilst prior work has shown the effects of statistical height variation to be negligible, variations in the posture of the target may still pose a significant source of potential error. Here we present a method that addresses this issue via the use of regression based pedestrian posture estimation. This is demonstrated for variations in pedestrian target height ranging from 0.4-2m over a distance to target range of 7-30m

    A photogrammetric approach for real-time 3D localization and tracking of pedestrians in monocular infrared imagery

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    Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. We propose an approach that challenges the current trend in complex tracking solutions by addressing this fundamental ambiguity head-on. In contrast to prior work in the field, we leverage the key advantages of thermal-band infrared (IR) imagery for the pedestrian localization to show that robust localization and foreground target separation, afforded via such imagery, facilities accurate 3D position estimation to within the error bounds of conventional Global Position System (GPS) positioning. This work investigates the accuracy of classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the scene. Based on photogrammetric estimation of target position, we then illustrate the efficiency of regular Kalman filter based tracking operating on actual 3D pedestrian scene trajectories. We present both a statistical and experimental analysis of the associated errors of this approach in addition to real-time 3D pedestrian tracking using monocular infrared (IR) imagery from a thermal-band camera. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

    Real-time classification of vehicle types within infra-red imagery.

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    Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

    Real-time Classification of Vehicle Types within Infra-red Imagery

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    Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

    Human Pose Estimation with Implicit Shape Models

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    This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions
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