A 3D Omnidirectional Sensor For Mobile Robot Applications

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Face Detection & Recognition based on Fusion of Omnidirectional & PTZ Vision Sensors and Heteregenous Database

International audienceLarge field of view with high resolution has always been sought-after for Mobile Robotic Authentication. So the Vision System proposed here is composed of a catadioptric sensor for full range monitoring and a Pan Tilt Zoom (PTZ) camera together forming an innovative sensor, able to detect and track any moving objects at a higher zoom level. In our application, the catadioptric sensor is calibrated and used to detect and track Regions Of Iinterest (ROIs) within its 360 degree Field Of View (FOV), especially face regions. Using a joint calibration strategy, the PTZ camera parameters are automatically adjusted by the system in order to detect and track the face ROI within a higher resolution and project the same in faces-pace for recognition via Eigenface algorithm. Face recognition is one important task in Nomad Biometric Authentication (NOBA 1) project. However, as many other face databases, it will easily produce the Small Sample Size (SSS) problem in some applications with NOBA data. Thus this journal uses the compressed sensing (CS) algorithm to solve the SSS problem in NOBA face database. Some experiments can prove the feasibility and validity of this solution. The whole development has been partially validated by application to the Face recognition using our own database NOBA

REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed

Real Time Object Detection, Tracking, and Distance and Motion Estimation based on Deep Learning: Application to Smart Mobility

International audienceIn this paper, we will introduce our object detection, localization and tracking system for smart mobility applications like traffic road and railway environment. Firstly, an object detection and tracking approach was firstly carried out within two deep learning approaches: You Only Look Once (YOLO) V3 and Single Shot Detector (SSD). A comparison between the two methods allows us to identify their applicability in the traffic environment. Both the performances in road and in railway environments were evaluated. Secondly, object distance estimation based on Monodepth algorithm was developed. This model is trained on stereo images dataset but its inference uses monocular images. As the output data, we have a disparity map that we combine with the output of object detection. To validate our approach, we have tested two models with different backbones including VGG and ResNet used with two datasets : Cityscape and KITTI. As the last step of our approach, we have developed a new method-based SSD to analyse the behavior of pedestrian and vehicle by tracking their movements even in case of no detection on some images of a sequence. We have developed an algorithm based on the coordinates of the output bounding boxes of the SSD algorithm. The objective is to determine if the trajectory of a pedestrian or vehicle can lead to a dangerous situations. The whole of development is tested in real vehicle traffic conditions in Rouen city center, and with videos taken by embedded cameras along the Rouen tramway

TRAJECTOGRAPHY ESTIMATION FOR A SMART POWERED WHEELCHAIR ORB-SLAM2 VS RTAB-MAP A PREPRINT

International audienceThis work is part of the ADAPT project relating to the implementation of a trajectography functionality that aims to measure the path travelled by a patient during the clinical trials. This system (hardware and software) must be reliable, quickly integrable, low-cost and real-time. Therefore, our choices have been naturally made towards visual SLAM-based solutions coupled with an Intel real-sense consumer sensors. This paper is a comparison of two well-known visual SLAM algorithms in the scientific community, ORB-SLAM2 and RTAB-Map, evaluated in different path configurations. The added value of our work lies in the accurate estimation of the trajectories achieved through the use of a VICON motion capture system

Face Detection & Recognition based on Fusion of Omnidirectional & PTZ Vision Sensors and Heteregenous Database

International audienceLarge field of view with high resolution has always been sought-after for Mobile Robotic Authentication. So the Vision System proposed here is composed of a catadioptric sensor for full range monitoring and a Pan Tilt Zoom (PTZ) camera together forming an innovative sensor, able to detect and track any moving objects at a higher zoom level. In our application, the catadioptric sensor is calibrated and used to detect and track Regions Of Iinterest (ROIs) within its 360 degree Field Of View (FOV), especially face regions. Using a joint calibration strategy, the PTZ camera parameters are automatically adjusted by the system in order to detect and track the face ROI within a higher resolution and project the same in faces-pace for recognition via Eigenface algorithm. Face recognition is one important task in Nomad Biometric Authentication (NOBA 1) project. However, as many other face databases, it will easily produce the Small Sample Size (SSS) problem in some applications with NOBA data. Thus this journal uses the compressed sensing (CS) algorithm to solve the SSS problem in NOBA face database. Some experiments can prove the feasibility and validity of this solution. The whole development has been partially validated by application to the Face recognition using our own database NOBA

Detection, Localisation et Tracking d’Objets basé Deep Learning pour un Fauteuil Roulant Intelligent.

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A Dynamic Programming Algorithm Applied to Omnidirectional Vision for Dense 3D Reconstruction

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Robust Radial Face Detection for Omnidirectional Vision

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Biometrie authentication platform-based multisensor fusion

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