Kinematic Spline Curves: A temporal invariant descriptor for fast action recognition

International audienc

Vers une reconnaissance en ligne d'actions à partir de caméras RGB-D

International audienc

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

International audienc

3D real-time human action recognition using a spline interpolation approach

International audienc

Véhicules intelligents (étude et développement d'un capteur intelligent de vision pour l'attelage virtuel)

Si de nombreux aspects de notre vie sont devenus plus agréables grâce à l'utilisation de technologies avancées, il a fallu longtemps au secteur du transport pour combler son retard en la matière. Aujourd'hui, ces progrès sont devenus incontournables. Ce travail décrit la perception de l'environnement à l'avant d'un véhicule, sur la base d'un capteur stéréoscopique conçu et mis en place en s'appuyant sur le concept de capteur intelligent afin de réaliser un Attelage Virtuel. Après une présentation de la problématique associée, le premier chapitre dresse l'état de l'art en matière de véhicules intelligents. Le second introduit la notion de capteur intelligent et présente les approches de conception que nous mettons en application pour identifier les différents services et fonctionnalités que doit intégrer ce capteur stéréoscopique intelligent pour contribuer à la réalisation de la tâche d'Attelage Virtuel. Le dernier chapitre expose la réalisation du capteur stéréoscopique. Nous y détaillons les problèmes que posent l'application de la stéréovision au domaine des transports et les solutions que nous y avons apportées. Ainsi, sont évoquées les difficultés posées par la phase de calibration, l'extraction en temps réel des zones d'intérêt et le problème de certification des données obtenues. Le respect des contraintes temporelles nous a conduit à mettre en oeuvre un dispositif d'extraction et de tracking. Les performances de chacun des modules constitutifs de notre capteur sont étayées par des résultats expérimentaux obtenus en situation réelle. Enfin, nous présentons une technique permettant le suivi du véhicule avec une seule caméra. .LILLE1-BU (590092102) / SudocSudocFranceF

Distances evolution analysis for online and off-line human object interaction recognition

International audienceHuman action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and On-line RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discrimina-tive for human object interaction classification as demonstrated here

Distances evolution analysis for online and off-line human object interaction recognition

International audienceHuman action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and On-line RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discrimina-tive for human object interaction classification as demonstrated here

Distances evolution analysis for online and off-line human object interaction recognition

International audienceHuman action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and On-line RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discrimina-tive for human object interaction classification as demonstrated here

Detection of Abnormal Gait From Skeleton Data

International audienceHuman gait analysis has becomes of special interest to computer vision community in recent years. The recently developed commodity depth sensors bring new opportunities in this domain.In this paper, we study the human gait using non intrusive sensors (Kinect 2) in order to classify normal human gait and abnormal ones. We propose the evolution of inter-joints distances as spatio temporal intrinsic feature that have the advantage to be robust to location. We achieve 98% success to classify normal and abnormal gaits and show some relevant features that are able to distinguish them

Human object interaction recognition using rate-invariant shape analysis of inter joint distances trajectories

International audienceHuman action recognition has emerged as one of the most challenging and active areas of research in the computer vision domain. In addition to pose variation and scale variability, high complexity of human motions and the variability of object interactions represent additional significant challenges. In this paper, we present an approach for human-object interaction modeling and classification. Towards that goal, we adopt relevant frame-level features; the inter-joint distances and joints-object distances. These proposed features are efficiently insensitive to position and pose variation. The evolution of the these distances in time is modeled by trajectories in a high dimension space and a shape analysis framework is used to model and compare the trajectories corresponding to human-object interaction in a Riemannian manifold. The experiments conducted following state-of-the-art settings and results demonstrate the strength of the proposed method. Using only the skeletal information , we achieve state-of-the-art classification results on the benchmark dataset