Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201

Incorporating Wheelchair Users in People Detection

A wheelchair users detector is presented to extend people detection, providing a more general solution to detect people in environments such as houses adapted for independent and assisted living, hospitals, healthcare centers and senior residences. A wheelchair user model is incorporated in a detector whose detections are afterwards combined with the ones obtained using traditional people detectors (we define these as standing people detectors). We have trained a model for classical (DPM) and for modern (Faster-RCNN) detection algorithms, to compare their performance. Besides the extensibility proposed with respect to people detection, a dataset of video sequences has been recorded in a real in-door senior residence environment containing wheelchairs users and standing people and it has been released together with the associated groundtruthThis work has been partially supported by the Spanish government under the project TEC2014-53176-R (HAVideo) and by the Spanish Government FPU grant programme (Ministerio de Educación, Cultura y Deporte

Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)

Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs

Calibration-free Pedestrian Partial Pose Estimation Using a High-mounted Kinect

Les applications de l’analyse du comportement humain ont subit de rapides développements durant les dernières décades, tant au niveau des systèmes de divertissements que pour des applications professionnelles comme les interfaces humain-machine, les systèmes d’assistance de conduite automobile ou des systèmes de protection des piétons. Cette thèse traite du problème de reconnaissance de piétons ainsi qu’à l’estimation de leur orientation en 3D. Cette estimation est faite dans l’optique que la connaissance de cette orientation est bénéfique tant au niveau de l’analyse que de la prédiction du comportement des piétons. De ce fait, cette thèse propose à la fois une nouvelle méthode pour détecter les piétons et une manière d’estimer leur orientation, par l’intégration séquentielle d’un module de détection et un module d’estimation d’orientation. Pour effectuer cette détection de piéton, nous avons conçu un classificateur en cascade qui génère automatiquement une boîte autour des piétons détectés dans l’image. Suivant cela, des régions sont extraites d’un nuage de points 3D afin de classifier l’orientation du torse du piéton. Cette classification se base sur une image synthétique grossière par tramage (rasterization) qui simule une caméra virtuelle placée immédiatement au-dessus du piéton détecté. Une machine à vecteurs de support effectue la classification à partir de cette image de synthèse, pour l’une des 10 orientations discrètes utilisées lors de l’entrainement (incréments de 30 degrés). Afin de valider les performances de notre approche d’estimation d’orientation, nous avons construit une base de données de référence contenant 764 nuages de points. Ces données furent capturées à l’aide d’une caméra Kinect de Microsoft pour 30 volontaires différents, et la vérité-terrain sur l’orientation fut établie par l’entremise d’un système de capture de mouvement Vicon. Finalement, nous avons démontré les améliorations apportées par notre approche. En particulier, nous pouvons détecter des piétons avec une précision de 95.29% et estimer l’orientation du corps (dans un intervalle de 30 degrés) avec une précision de 88.88%. Nous espérons ainsi que nos résultats de recherche puissent servir de point de départ à d’autres recherches futures.The application of human behavior analysis has undergone rapid development during the last decades from entertainment system to professional one, as Human Robot Interaction (HRI), Advanced Driver Assistance System (ADAS), Pedestrian Protection System (PPS), etc. Meanwhile, this thesis addresses the problem of recognizing pedestrians and estimating their body orientation in 3D based on the fact that estimating a person’s orientation is beneficial in determining their behavior. In this thesis, a new method is proposed for detecting and estimating the orientation, in which the result of a pedestrian detection module and a orientation estimation module are integrated sequentially. For the goal of pedestrian detection, a cascade classifier is designed to draw a bounding box around the detected pedestrian. Following this, extracted regions are given to a discrete orientation classifier to estimate pedestrian body’s orientation. This classification is based on a coarse, rasterized depth image simulating a top-view virtual camera, and uses a support vector machine classifier that was trained to distinguish 10 orientations (30 degrees increments). In order to test the performance of our approach, a new benchmark database contains 764 sets of point cloud for body-orientation classification was captured. For this benchmark, a Kinect recorded the point cloud of 30 participants and a marker-based motion capture system (Vicon) provided the ground truth on their orientation. Finally we demonstrated the improvements brought by our system, as it detected pedestrian with an accuracy of 95:29% and estimated the body orientation with an accuracy of 88:88%.We hope it can provide a new foundation for future researches