nils-herrmann/clean_scirepeval huggingface dataset:
Pre-Fetching Strategies for Remote and Interactive Browsing of JPEG2000 Images

This paper considers the remote interactive browsing of large JPEG2000 images. In contrast with previous contributions, we focus on the dynamic nature of the system. Practically, we study the conditions under which a priori knowledge about the user behavior may help to improve the browsing system reactivity, when combined with appropriate pre-fetching mechanisms. In particular, our simulations show that, due to the latency inherent to client/server exchanges, a benefit can be drawn by scheduling future window of interest (WoI) data before all current WoI data have been sent to the client. They also reveal that an accurate knowledge of the user behavior is not necessary to get important improvements over conventional scheduling approaches.
Object recognition based on binary partition trees

This paper presents an object recognition method that exploits the representation of the images obtained by means of a binary partition tree (BPT). The shape matching technique in which it is based was first presented in F. Marques et al., (2002). This method compares a transformed version of an object shape model (reference contour) to the contours of a partition of the image. The comparison is based on a distance map that measures the Euclidean distance between any points in the image to the partition contours. In F. Marques et al., (2002), this algorithm was applied using a colour-based segmentation of the image and a full-search was performed to find the best match between the searched object and the contours of this segmentation. Here, the information of the binary partition tree is used both to obtain the segmentation and to guide and reduce the search for the optimum match between the shape and the objects of the image.
Cloud motion estimation in SEVIRI image sequences

Determination of atmospheric dynamic characteristics from remote sensing imagery is fundamental in weather and climate studies. The SEVIRI radiometer, on board the MSG, with its 12 bands and 15 minutes sensing capability provides an important amount of information for cloud tracking. In this work, we have first conducted a detailed evaluation of twelve region matching techniques in order to select those providing the best results. For this performance evaluation, databases of synthetic and real sequences have been used. Next, the best metrics have been incorporated in a new methodology that includes a preliminary stage that segments cloudy structures to initialize the optimum motion estimation parameters (template size and search window dimensions). Also a study region mask is generated to disable the application of the motion estimation algorithm in unreliable areas, thus, eliminating erroneous vectors and decreasing the computation times.
Scalable segmentation-based coding of video sequences addressing content-based functionalities

In this paper, we address video scalability in the framework of a region-based coding system, allowing content-based functionalities. The proposed algorithm can construct scaled layers from a video sequence, each one with either fixed bit-rate or fixed quality, allowing content based manipulation. Two modes of operation have been defined: a supervised mode, that allows the user to select the objects to be coded in the enhancement layer, and an unsupervised mode, where this selection is done by the algorithm itself.
Region-based mean shift tracking: Application to face tracking

We present a new technique for object tracking that is an extension of the mean shift tracking algorithm. The proposed technique relies on a segmentation of the area under analysis into a set of color-homogenous regions. The use of regions allows a robust estimation of the likelihood distributions that form the object and background models, as well as a precise shape definition of the object being tracked. Thanks to this accurate object definition, the object model can be updated through the tracking process, handling variations in the object representation. These concepts have been tested in the case of tracking human faces.
A region-based particle filter for generic object tracking and segmentation

In this work we present a region-based particle filter for generic object tracking and segmentation. The representation of the object in terms of regions homogeneous in color allows the proposed algorithm to robustly track the object and accurately segment its shape along the sequence. Moreover, this segmentation provides a mechanism to update the target model and allows the tracker to deal with color and shape variations of the object. The performance of the algorithm has been tested using the LabelMe Video public database. The experiments show satisfactory results in both tracking and segmentation of the object without an important increase of the computational time due to an efficient computation of the image partition.
Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image

In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the networks outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.
Overview of the performances and tracking design of the SIRAL altimeter for the CryoSat mission

This paper presents the principle of the measurements, the main system features and the basic ground processing of SIRAL. Simulations of the main performances in the different modes are also discussed including the bidimensional impulse response (range and azimuth). A major scientific requirement is to ensure the continuity of the measurements. This is particularly challenging for the on-board tracker over the steepest parts of the Antarctica. As a consequence a new tracking algorithm has been designed for SIRAL with the expertise of CNES. The algorithm has been derived from the analysis of simulated echoes using an Antarctica DEM and a radar echo simulator. The tracking design and performance are briefly described.
Towards automated map updating for mobile robot localization

In mobile robotics, in order to accomplish a task, self-localization is of paramount importance. It is generally granted that robot localization evolving in an outdoor environment needs an absolute localization technique, which can be map-based. While most of map-based techniques use a static map of the environment, we propose here to handle a dynamic map, which could originate from publicly available Geographical Information Systems (GIS) in which imprecise mapped landmarks would have their own localization updated, unobservable mapped landmarks would be deleted and newly observable landmarks would be added. In this article, our contribution is to demonstrate the possibility to delete dynamically repositionable landmarks in a dynamic environment map on a real vehicle online in real-time, using a partially separated vehicle localization integrity and landmark existence probabilities, based on a Bayesian graphical network formalism.
Nonparametric data association for particle filter based multi-object tracking: application to multi-pedestrian tracking

This article deals with the following issue: how to track a varying number of pedestrians through observations by means of a 4-plane laser sensor. In order to answer to the multiple target tracking problem and more specifically pedestrian tracking, we propose in this paper a statistical approach using a particle filter based on nonparametric data association methods. This approach allows to go beyond the conventional Gaussian assumption and to use as well as possible each particle during track/observation association by means of either a ldquoParzen Windowrdquo kernel method or a K-nearest neighbor algorithm. Simulated and experimental results show the relevance of this method compared to the usual Gaussian window methods.
Association of AdaBoost and Kernel based machine learning methods for visual pedestrian recognition

We present a real-time solution for pedestrian detection in images. The key point of such method is the definition of a generic model able to describe the huge variability of pedestrians. We propose a learning based approach using a training set composed by positive and negative samples. A simple description of each candidate image provides a huge feature vector from which can be built weak classifiers. We select a subset of relevant weak classifiers using a classic AdaBoost algorithm. The resulting subset is then used as binary vectors in a kernel based machine learning classifier (like SVM, RVM, ...). The major contribution of the paper is the original association of an AdaBoost algorithm to select the relevant weak classifiers, followed by a SVM like classifier for which input data are given by the selected weak classifiers. Kernel based machine learning provides non-linear separator into the weak classifier space while standard AdaBoost gives a linear one. Performances of this method are compared to state of art methods and a real-time application with a monocular camera embedded in a moving vehicle is also presented to match this approach against a real context.
Non-parametric occupancy map using millions of range data

This paper presents a fast method to estimate the probability of occupancy of a space point from a huge set of 3D rays represented in a common reference. These data can come from any range finding sensor such as : Lidar, Kinect or Velodyne. The key idea is to consider that the occupancy of a space 3D point is linked to 1) the number of 3D point belonging to a local volume around the point and 2) the number of rays crossing through the same volume. We propose a probabilistic non-parametric framework based on KNN estimator. The major contribution of the paper is an original solution to search rays in the neighborhood of a 3D point with a five dimensional binary tree that can handle several millions measurements. Experiments shows the relevance of the proposed method in terms of both accuracy and computation time. Moreover, the resulting method has been applied to three different 3D sensors: a Kinect, a 3D Lidar (Velodyne HDL-64E) and a mono-planar Lidar.
Pedestrian detection method using a multilayer laserscanner: Application in urban environment

Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we propose here a detection system based on the fusion of information located in the 4 laser planes. In this paper, we propose a Parzen kernel method that allows first to isolate the ldquopedestrian objectsrdquo in each plane and then to carry out a decentralized fusion according to the 4 laser planes. Finally, to improve our pedestrian detection algorithm we use a MCMC based PF method allowing a closer observation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our pedestrian detection algorithm in regard to a method using only a single-row laser-range scanner.
Generic realtime kernel based tracking

This paper deals with the design of a generic visual tracking algorithm suitable for a large class of camera (single viewpoint sensors). It is based on the estimation of the relationship between observations and motion on the sphere. This is efficiently achieved using a kernel-based regression function on a generic linearly-weighted sum of non-linear basis functions. We also present two set of experiments. The first one shows the efficiency of our algorithm through the tracking in video sequences acquired with three types of cameras (conventional, dioptric-fisheye and catadioptric). The real-time performances will be shown by tracking one or several planes. The second set of experiments presents an application of our tracking algorithm to visual servoing with a fisheye camera.
Visual pedestrian recognition inweak classifier space using nonlinear parametric models

Pedestrian recognition in images is a challenging task. Indeed a generic model must be able to describe the huge variability of pedestrians. We propose a learning based approach using a training set composed by positive and negative samples. A simple description of each candidate image provides a huge feature vector from which can be built weak classifiers. We select a subset of relevant weak classifiers using a classic AdaBoost algorithm. The resulting subset is then used as binary vectors in a kernel based machine learning classifier (like SVM, RVM, ...). The major contribution of the paper is the original association of an AdaBoost algorithm to select the relevant weak classifiers, followed by a SVM like classifier for which input data are given by the selected weak classifiers. Kernel based machine learning provides non-linear separator into the weak classifier space while standard AdaBoost gives a linear one. Performances of this method are compared to a classical AdaBoost method.
Augmented skeletal joints for temporal segmentation of sign language actions

We present in this paper a novel solution for temporal segmentation of human gestures that takes advantage of the skeletal-joints streams offered by the Kinect sensor. Our contribution consist in introducing an improved skeletal representation and its usage in a multilayer motion delimitation that distinguishes the non-vocabulary actions. The evaluation of the solution is presented on a subset of the Chalearn Gesture Challenge (CGC) 2014 dataset. The obtained temporal segmentation is better than the CGC baseline methods and has proved to be important for the task of human-action recognition.
Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification

This paper addresses real-time automatic visual tracking, labeling and classification of a variable number of objects such as pedestrians or/and vehicles, under time-varying illumination conditions. The illumination and multi-object configuration are jointly tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF). The measurement is provided by a static camera, associated to a basic foreground / background segmentation. As a first contribution, we propose in this paper to jointly track the light source within the Particle Filter, considering it as an additionnal object. Illumination-dependant shadows cast by objects are modeled and treated as foreground, thus avoiding the difficult task of shadow segmentation. As a second contribution, we estimate object category as a random variable also tracked within the Particle Filter, thus unifying object tracking and classification into a single process. Real time tracking results are shown and discussed on sequences involving various categories of users such as pedestrians, cars, light trucks and heavy trucks.
Poseidon 2, the new generation altimeter for JASON mission

Poseidon 2 is the new spaceborne altimeter designed to follow the successful TOPEX Poseidon altimetry mission. The follow-on mission JASON is under preparation and is planned to be operational in early 2000. Alcatel Space Industrie, under CNES contract, completes the engineering and qualification model and 2 flight models. This paper provides a brief overview of the instrument and describes the results obtained during the flight model ground tests. Hardware, new subsystem characteristics and primary performance results are presented.
Vehicle trajectories evaluation by static video sensors

Metrology of vehicle trajectories has several applications in the field of road safety, particularly in dangerous curves. Actually, it is of great interest to observe trajectories of vehicles with the aim of designing a real time driver warning device in dangerous areas. This paper addresses the first step of a work with a video system placed along the road with the objective of vehicle's position and speed estimation. This system has been totally developed for this project and can record simultaneously three cameras with 640 times 480 pixels up to 30 frames per second (fps) and rangefinder informations. The best contribution of this paper is an original probabilistic background subtraction algorithm, first step of a global method (calibration, tracking, ...) implemented to be able to measure vehicle trajectories. Kinematic GPS (in post-processing) has been extensively used to get ground truth
Real-time SLAM for static multi-objects learning and tracking applied to augmented reality applications

This paper presents a new approach for multi-objects tracking from a video camera moving in an unknown environment. The tracking involves static objects of different known shapes, whose poses and sizes are determined online. For augmented reality applications, objects must be precisely tracked even if they are far from the camera or if they are hidden. Camera poses are computed using simultaneous localization and mapping (SLAM) based on bundle adjustment process to optimize problem parameters. We propose to include in an incremental bundle adjustment the parameters of the observed objects as well as the camera poses and 3D points. We show, through the example of 3D models of basics shapes (planes, parallelepipeds, cylinders and spheres) coarsely initialized online using a manual selection, that the joint optimization of parameters constrains the 3D points to approach the objects, and also constrains the objects to fit the 3D points. Moreover, we developed a generic and optimized library to solve this modified bundle adjustment and demonstrate the high performance of our solution compared to the state of the art alternative. Augmented reality experiments in realtime demonstrate the accuracy and the robustness of our method.
Faster R-CNN Scene Specialization with a Sequential Monte-Carlo Framework

The performance of the learning-based detector depends much on its training dataset and decreases rapidly when it is tested on a new scene. The reason is that in the large variations between the source training dataset and target scene. To solve this problem, we propose a novel approach to automatically specialize a generic detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN deep model. The main idea is to consider the Faster R-CNN as a function that generates realizations from the probability distribution of the object to be detected in the target sequence. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized Faster R-CNN estimated in a sequential Bayesian filter framework. The resulting algorithm is compared to the state of the art scene specialization methods on several challenging datasets, the results are very promising.
Parallel Implementation of Modified 2D Pattern Matching

This paper describes a highly efficient and parallelized HW implementation of multiple modules processing a pattern matching algorithm of a 2D affine model object applied to the detection and tracking of an autonomous vehicle. The pattern matching problem has been adapted to the edge detection of the rear side of a Cycab vehicle modeled by the centroid and scale parameters. Reordering of the pattern and online generation of pixels of interest are the main contributions. The computing time for an image is independent of the number of pattern matching processing modules instantiated in hardware, making feasible to use them in large filters. The maximum operation frequency achieved has reached up to 103.1 MHz with minimal use of resources. An implementation of 200 parallel modules is capable to process 200 frames per second for image sizes of 512x512 pixels while equivalent program in a desktop PC executes only 4 fps.
Siral the radar altimeter for the cryosat mission, pre-launch performances

The development phase of the SAR Interferometer Radar Altimeter (SIRAL) is in the final stage in Alcatel Space and during year 2004 the Engineering and the Flight Models is tested before delivery to the Mission Prime ASTRIUM GmbH. This ESA mission will be used to estimate - on a global scale - the fluctuations in mass of sea-ice and land-ice. This paper gives a detailed presentation of the microwave and processing units developed, followed by a survey of the pre-launch performances.
Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.
A method based on multilayer laserscanner to detect and track pedestrians in urban environment

This article deals with the detection and tracking of pedestrians by means of a laser sensor. This sensor placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in urban environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we use a detection system based on the fusion of information located in the 4 laser planes. A “Parzen Window” kernel method is described and allows the centralized fusion of different planes. Finally to improve our pedestrian detection algorithm we use a particle filter with feedback in laser image allowing a closer obervation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our algorithm in regard to proposed method.