A mathematical explanation via "intelligent" PID controllers of the strange ubiquity of PIDs

The ubiquity of PID controllers in the industry has remained mysterious until now. We provide here a mathematical explanation of this strange phenomenon by comparing their sampling with the the one of "intelligent" PID controllers, which were recently introduced. Some computer simulations nevertheless confirm the superiority of the new intelligent feedback design

Visual object categorization with new keypoint-based adaBoost features

We present promising results for visual object categorization, obtained with adaBoost using new original ?keypoints-based features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Preliminary tests on a small subset of a pedestrians database also gives promising 97% recall with 92 % precision, which shows the generality of our new family of features. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt? in the case of lateral-cars) and thus have a ?semantic? meaning. We also made a first test on video for detecting vehicles from adaBoostselected keypoints filtered in real-time from all detected keypoints

CoreBot M : Le robot de la Team CoreBots préparé pour l'édition 2011 du défi Carotte

3 pagesNational audienceLa deuxième édition du défi CAROTTE, organisé par l'ANR et la DGA, aura lieu fin juin à Bourges. Son objectif : vérifier la capacité de petits robots terrestres pour des missions de reconnaissance en environnement intérieur. Après avoir remporté l'édition 2010, la Team CoreBots présente son nouveau robot dédié à l'édition 2011. Sans en révéler tous les secrets, ce papier expose les grandes lignes de notre architecture et de notre stratégie

Interest points harvesting in video sequences for efficient person identification

International audienceWe propose and evaluate a new approach for identification of persons, based on harvesting of interest point descriptors in video sequences. By accumulating interest points on several sufficiently time-spaced images during person silhouette or face tracking within each camera, the collected interest points capture appearance variability. Our method can in particular be applied to global person re-identification in a network of cameras. We present a first experimental evaluation conducted on a publicly available set of videos in a commercial mall, with very promising inter-camera pedestrian reidentification performances (a precision of 82% for a recall of 78%). Our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making re-identification among hundreds of persons computationally feasible in less than ~ 1/5s second. Finally, we also present a first feasibility test for on-the-fly face recognition, with encouraging results

AdaBoost with "keypoint presence features" for real-time vehivle visual detection

International audienceWe present promising results for real-time vehicle visual detection, obtained with adaBoost using new original “keypoints presence features”. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a “keypoint” (~ a SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as “wheel” or “side skirt”) and thus have a “semantic” meaning

Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences

International audienceWe present and evaluate a person re-identification scheme for multi-camera surveillance system. Our approach uses matching of signatures based on interest-points descriptors collected on short video sequences. One of the originalities of our method is to accumulate interest points on several sufficiently time-spaced images during person tracking within each camera, in order to capture appearance variability. A first experimental evaluation conducted on a publicly available set of low-resolution videos in a commercial mall shows very promising inter-camera person re-identification performances (a precision of 82% for a recall of 78%). It should also be noted that our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making reidentification among hundreds of persons computationally feasible in less than ~ 1/5 second

Vidéosurveillance intelligente : ré-identification de personnes par signature utilisant des descripteurs de points d'intérêt collectés sur des séquences

National audienceNous présentons et évaluons une méthode de ré-identification de personnes pour les systèmes de surveillance multicaméras. Notre approche utilise la mise en correspondance de signatures fondées sur les descripteurs de points d'intérêt collectés sur de courtes séquences vidéos. Une des originalités de notre travail est d'accumuler les points d'intérêt à des instants suffisamment espacés durant le suivi de personne, de façon à capturer dans la signature la variabilité d'apparence des personnes. Une première évaluation expérimentale a été effectuée sur une base publique d'enregistrements à basse résolution dans un centre commercial, et les performances de re-identification sont très prometteuses (une précision de 82% pour un rappel de 78%). De plus, notre technique de ré-identification est particulièrement rapide : ~1/8 s pour une requête à comparer à 10 personnes vues précédemment, et surtout une dépendance logarithmique avec le nombre de modèles stockés, de sorte que la ré-identification parmi des milliers de personnes prendrait moins de ¼ s de calcul

Commande sans modèle de la vitesse longitudinale d'un véhicule électrique

International audienceWe present for the longitudinal control of an electrical vehicle a "model-free'' control strategy, which is illustrated by convincing experimental results. The physical control is a coefficient rate of the maximal voltage of the battery. The chassis and the engine dynamical equations exhibit complex unknown parameters and/or neglected terms. The proposed ``intelligent'' PI controller, which utilizes new algebraic techniques for estimating derivatives of noisy signals, permits to bypass those parameter and model uncertainties, without the necessity of identifying them

Odometry from Planar landmarks

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Planar landmark detection using a specific arrangement of laser scanners

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