nils-herrmann/clean_scirepeval huggingface dataset:
SIRAL: the radar altimeter for CryoSat mission, under development

SIRAL (SAR Interferometer Radar Altimeter) is the new spaceborne altimeter designed for CryoSat mission. The instrument is currently in the development phase, in ALCATEL SPACE, which encompasses an Engineering Model and a Flight Model. This ESA mission, planned for 2004, will be used to estimate - on a global scale - the fluctuations in mass of sea-ice and land-ice. This paper discusses the flexibility of the instrument and in particular its capability to operate in various modes (conventional altimeter, SAR and interferometer modes), and gives main results from the pre-developments and breadboard activities.
Modular Ensemble Tracking

Object Tracking is a very important domain in computer vision. It was recently approached using classification techniques and still more recently using boosting methods. Boosting is a general method of producing an accurate prediction rule by combining rough and moderately inaccurate ones. We introduce in this paper a modular object tracking algorithm based on one of these boosting methods: Adaboost. Tracking is performed on homogeneous feature spaces and the final classification decision is obtained by combining the decisions made on each of these spaces. A classifier update stage is also introduced, that allows the method both to handle time-varying objects in real-time (using fast computable features) and to handle partial occlusions. We compare the performance of our algorithm with Ensemble Tracking algorithm  on several real video sequences.
Unifying real-time multi-vehicle tracking and categorization

This paper addresses real-time automatic visual tracking and classification of a variable number of vehicles in traffic. This off-board surveillance device may cooperate with on-board Advanced Driver Assistance Systems (ADAS), extending its measurement range to the areas of the road that are not in the car sensors field-of-view (in a curve or an intersection). Tracking results also are useful for statistical trajectory analysis, devoted to understanding and improving user-user and user-infrastructure interactions. As a main contribution, this paper proposes to unify vehicle tracking and classification in a single processing step. This paper also addresses a vehicle anisotropic distance measurement based on the vehicle 3D geometric model. Real time tracking results are shown and discussed on road sequences involving various types of vehicles such as motorcycles, cars, light trucks and heavy trucks.
Localization in urban environments: monocular vision compared to a differential GPS sensor

In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then a 3D reconstruction of the path and the environment is computed off line from the learning sequence. The 3D reconstruction is then used for computing the pose of the robot in real time (30 Hz) in autonomous navigation. Results from our localization method are compared to the ground truth measured with a differential GPS.
IPCC algorithm: Moving object detection in 3D-Lidar and camera data

In this paper, we present a new algorithm named IPCC for Iterative Photo Consistency Check. The goal of this one is to detect a posteriori moving object in both camera and range data. The range data may be provided by different sensor such as: Riegl, Kinect or Velodyne with no distinction. The key idea is to consider that range data acquired on static objects are photo-consistent, they have the same color and texture in all the camera images, but range data acquired on moving object are not photo-consistent. The main problem is to take in account that range finding sensor and camera are not synchronous, so what is seen in camera is not what range finding sensors acquires. The major contribution in this paper is an original way to find non photo-consistent range data using the camera images in an erosion process of the scene. Experiments show the relevance of the proposed method in terms of both accuracy and computation time.
New generation of RF unit for radar altimeter

Alcatel developed a new generation of dual-frequency radar altimeter (POSEIDON 2) which works in C and Ku band. This article discusses about the radiofrequency unit (RFU) which plays a major role for the stringent performances of the altimeter. The compact RF unit has been designed with a modular concept. It uses the new advanced technologies: extensively use of MMIC, pHemt LNA and hybrid HFET power amplifiers. The transmitted peak power level is greater than 23W in C band and 7W in Ku band.
A regression-based approach to recover human pose from voxel data

This paper deals with human body pose recovery from multiple cameras, which is a key task in monitoring of human activity. This regression-based approach relies on a 3D description of a body voxel reconstruction, combined with a decomposition of the estimation, which allows to recover a wide range of poses using synthetic training data. The precision of the proposed shape descriptor is quantitatively evaluated on synthetic data for a ground truth comparison, while the effectiveness of the whole system is qualitatively demonstrated on various real sequences.
Parzen method for fusion of laserscanner data: Application to pedestrian detection

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 horizontal 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 this article propose a detection system based on the fusion of information located in the 4 horizontal laser planes. A "Parzen Window" kernel method is described and allows the centralized fusion of different planes. 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.
Learning of binocular fixations using anomaly detection with deep reinforcement learning

Due to its ability to learn complex behaviors in high-dimensional state-action spaces, deep reinforcement learning algorithms have attracted much interest in the robotics community. For a practical reinforcement learning implementation on a robot, it has to be provided with an informative reward signal that makes it easy to discriminate the values of nearby states. To address this issue, prior information, e.g. in the form of a geometric model, or human supervision are often assumed. This paper proposes a method to learn binocular fixations without such prior information. Instead, it uses an informative reward requiring little supervised information. The reward computation is based on an anomaly detection mechanism which uses convolutional autoencoders. These detectors estimate in a weakly supervised way an object's pixellic position. This position estimate is affected by noise, which makes the reward signal noisy. We first show that this affects both the learning speed and the resulting policy. Then, we propose a method to partially remove the noise using regression on the detection change given sensor data. The binocular fixation task is learned in a simulated environment on an object training set with various shapes and colors. The learned policy is compared with another one learned with a highly informative and noiseless reward signal. The tests are carried out on the training set and on a test set of new objects. We observe similar performances, showing that the environment-encoding step can replace the prior information.
A Real-Time Map Refinement Method Using a Multi-Sensor Localization Framework

In today’s world, automatic navigation for a robotic device (autonomous vehicle and robot) is a pre-requisite for many complex tasks, which requires a robust localization method. We focus in this paper on the topic of localizing such a robot into an absolute and imprecise map. We propose a multi-sensor self-localization method, which is simultaneously able to operate with an imprecise map, as well as to improve the precision of an already existing one. The method uses split covariance intersection filter as well as an a priori selection of the best informative measurements out of all possible measurement sources at each time step. This selection scheme is based on an “added Shannon information”-based criterion. We demonstrate in operation via statistical analysis the consistency of a refined map obtained from a biased map while keeping vehicle localization integrity. On top of this, we demonstrate solving of the so-called kidnapped-robot problem using the same framework.
An augmented representation of activity in video using semantic-context information

Learning and recognizing activity in videos is an especially important task in computer vision. However, it is hard to perform. In this paper, we propose a new method by combining local and global context information to extract a bag-of-words-like representation of a single space-time point. Each spacetime point is described by a bag of visual words that encodes its relationships with the remaining space-time points in the video, defining the space-time context. Experiments on the KTH benchmark of action recognition, show that our approach performs accurately compared to the state-of-the-art.
Coarse localization using space-time and semantic-context representations of geo-referenced video sequences

We introduce a new video contents description approach and use it for the purpose of coarse localization. It is based on a Bag of Words representation combining both space-time STIP features and semantic-context SSC features. We assume that adding semantic context encodes in a more efficient way the spatio-temporal information into video sequences. The resulting augmented descriptor is related to a geographic location that can be estimated within a classic classification framework. We show that on real geo-referenced video sequences, the proposed system improves the localization compared to classical descriptors.
Unsupervised facial expressions recognition and avatar reconstruction from kinect

This paper presents a solution capable of recognizing the facial expressions performed by a person's face and mapping them to a 3D face virtual model using the depth and RGB data captured from Microsoft's Kinect sensor. This solution starts by detecting the face and segmenting its regions, then, it identifies the actual expression using EigenFaces metrics on the RGB images and reconstructs the face from the filtered Depth data. A new dataset relative to 20 human subjects is introduced for learning purposes. It contains the images and point clouds for the different facial expressions performed. The algorithm seeks and displays automatically the seven state of the art expressions including surprise, fear, disgust, anger, joy, sadness and the neutral appearance. As result our system shows a morphing sequence between the sets of 3D face avatar models.
Real time tracking with occlusion and illumination variations

The authors present a robust real time object tracking by image processing. A planar model defined by a center and few interest points of the object is used. This model choice allows to be robust to light variations and partial occlusions and a particle filter based tracker allows to recover from total occlusion of the object during a few seconds. This algorithm works at 15 Hz on a personal computer under Linux and is robust concerning both geometrical aspects as planar rotation and scale modification. Real experimentations prove the validity of the method for outdoor applications like vehicle tracking for adaptive cruise control.
DCSLAM: A dynamically constrained real-time slam

We propose a new real-time CSLAM algorithm (Constrained Simultaneous Localization And Mapping), named DCSLAM (dynamically constrained SLAM), designed to dynamically adapt each optimization to the variable number of parameters families and heterogeneous constraints. An automatic method is used to generate, from an exhaustive list of constraints, a dedicated optimization algorithm. This is, to our knowledge, the only implementation that combines flexibility and performance. The proposed experiments show the effectiveness of our approach in terms of accuracy and execution time compared to the state of the art on several public benchmarks of varying complexity. An augmented reality application mixing heterogeneous objects and constraints is also available.
Robust localization using a top-down approach with several LIDAR sensors

Robotic devices gain more and more use for performing tasks that are more and more complex. In the case of a mobile robot, one of the pre-requisites for doing so is to have a way of knowing its state and a knowledge about the neighbouring environment. This is generally adressed by a localization strategy. This paper adresses a robust Top-Down real-time localization scheme based on a Bayes Network and using only LIDAR range finders in a urban-like environment. This localization scheme is able to focus on the most informative observable landmark, one at a time thus reducing computational burden. We focus mainly on the improvements made in precision from using one LIDAR towards using 5 LIDARs, thus showing the capability of our approach to cope with several sensors in the same scheme as for one. Experiments are achieved on real data to evaluate the precision of the localization and the computation time.
An original correlation and data fusion based approach to detect a reap limit into a gray level image

Real time vision systems in an outdoor environment involve an increase of the algorithm complexity in order to solve the application with an acceptable autonomy. In our application of help guidance for agricultural mobile machines, the vehicle has to follow the limit between a mowed and an unmowed natural zone. After the extraction of the image by a CCD camera, a low level algorithm computes some luminance and texture parameters for each elementary site. We define a correlation function for each parameter. The original feature of our work is a geometrical characterization of the correlation functions, as well as performs belief functions associated with each parameter. The uncertainty notion, expressed by the use of the theory of evidence allows the control of the reliability of the estimation. This approach is very important in outdoor environment where the system can be confronted in many situations.
Improving existing cascaded face classifier by adding occlusion handling

Recent face detectors used in human robot interaction are boosted cascades. These cascades can detect upright faces but are very sensible to occlusions. We propose a generic framework to handle occlusions at prediction time in a boosted cascade. The contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: (1) the approximation of posterior probabilities on each level and (2) the computation of thresholds on these probabilities to make a decision. Both problems are studied and solutions are proposed and evaluated. The method is then applied on the problem of occluded faces detection. Experimental results are provided on classic databases to evaluate the proposed solution related to the basic one.
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.
M2SIR: A multi modal sequential importance resampling algorithm for particle filters

We present a multi modal sequential importance resampling particle filter algorithm for object tracking. We consider a hidden state sequence linked to several observation sequences given by different sensors. In a particle filter based framework, each sensor provides a likelihood (weight) associated to each particle and simple rules are applied to merge the different weights such as addition or product. We propose an original algorithm based on likelihood ratios to merge the observations within the sampling step. The algorithm is compared with classic fusion operations on toy examples. Moreover, we show that the method gives satisfactory results on a real vehicle tracking application.
MCMC Particle Filter for Real-Time Visual Tracking of Vehicles

This paper adresses real-time automatic tracking and labeling of a variable number of vehicles, using one or more still cameras. The multi-vehicle configuration is tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF) method. We show that integrating a simple vehicle kinematic model within this tracker allows to estimate the trajectories of a set of vehicles, with a moderate number of particles, allowing frame-rate computation. This paper also adresses vehicle tracking involving occlusions, deep scale and appearance changes: we propose a global observation function allowing to fairly track far vehicles as well as close vehicles. Experiment results are shown and discussed on multiple vehicle tracking sequences. Though now only tracking light vehicles, the ultimate goal of this research is to track and classify all classes of road users, also including trucks, cycles and pedestrians, in order to analyze road users interactions.
Deep edge-color invariant features for 2D/3D car fine-grained classification

Given an untextured 3D car models dataset, are we able to learn a robust make and model classifier which will be applied on real color images? One solution consists in finding a common representation between synthetic edge images and real color images. To address this issue, we introduce novel edge-color invariant features for 2D/3D car fine-grained classification. These features are learned simultaneously on real color and edge images of cars using a deep architecture. Using these accurate features, we propose to learn on edges synthetic images a fine-grained classifier which will be afterwards applied to real color images. The 3D car models dataset allows to automatically generate multi-views synthetic images using non-photorealistic edge rendering. Experimentally, we show efficiency of our learned edge-color invariant features for make and model recognition.
SIRAL, a high spatial resolution radar altimeter for the Cryosat mission

SIRAL (SAR Interferometer Radar Altimeter) is the new spaceborne altimeter designed for CryoSat mission. This ESA mission, planned for 2004, will be used to estimate-on a global scale- the fluctuations in mass of sea-ice and land-ice. The novelty of SIRAL concept with respect to conventional pulse-limited altimeter, is the implementation of Doppler processing for along-track resolution enhancement and also of interferometry, used to locate the echo in the across-track direction. The innovative technical features of SIRAL are presented hereafter with regards to the function requirements and also the expected performance.
Comparison between GMM and KDE data fusion methods for particle filtering: Application to pedestrian detection from laser and video measurements

In urban environment, pedestrian detection is a challenging task in automotive research, which often suffers from the lack of reliability due to the occurrences of spurious detections. In order to answer multitarget multisensor tracking problem and more specifically pedestrian tracking, we propose to use an algorithm based on a stochastic recursive Bayesian framework also called particle filter. We aim to solve the problem of consistent Bayesian Decentralized Data Fusion (BDDF) with particle filter using two different statistics approaches in order to better represent the particle set and maintains an accurate summary of the particles. We propose a comparison between a Kernel Density Estimation (KDE) based on non-parametric estimation and a Gaussian Mixture Model (GMM) based on parametric estimation. This approach allows to cope with non-linear models and multi-modalities induced by occlusions and clutters. These two algorithms differ in the representation of particle set during data fusion. Simulation results as well as the results of the experiments conducted on real data demonstrate the relevance of these approaches.
Robustness and repeatability of saliency models subjected to visual degradations

The present study investigates the sensitivity of computationnal models of visual attention when subjected to visual degradations. One hundred and twenty natural color pictures were degraded using 6 filtering operations. By using different settings, five state-of-the-art models are used to compute 11400 saliency maps. The comparison of these maps to human saliency maps indicates that the tested models are robust to most of the visual degradations they were subjected to. These findings have implications on saliency-based applications, such as quality assessment and coding. A last point concerns the high repeatability of saliency models that might be used in a context of image retrieval.
Age-dependent saccadic models for predicting eye movements

How people look at visual information reveals fundamental information about themselves, their interests and their state of mind. While previous visual attention models output static 2-dimensional saliency maps, saccadic models predict not only what observers look at but also how they move their eyes to explore the scene. Here we demonstrate that saccadic models are a flexible framework that can be tailored to emulate the gaze patterns from childhood to adulthood. The proposed age-dependent saccadic model not only outputs human-like, i.e. age-specific visual scanpath, but also significantly outperforms other state-of-the-art saliency models.
Your gaze betrays your age

Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6–10 year-old, 4–6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% of the time. The use of predicted salience does not further improve the classification's accuracy.
Spatio-temporal combination of saliency maps and eye-tracking assessment of different strategies

The modeling of the human visual attention into a computational attention model leads to the split of visual features into several independent channels. Then, a difficult problem arises to combine these maps, having different dynamic ranges or distribution. When several maps are considered, such process is mandatory in order to compute a single measure of interest for each location, regardless of which features contributed to the salience. Several strategies of cue combination are proposed in this paper for the spatial cues as well as the temporal saliency. Finally, some user tests on still image and video databases leads to highlight one operator.