Drone navigation and license place detection for vehicle location in indoor spaces

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   Résumé scientifique

   Drones have been widely utilized in various fields, but the number of drones being used illegally and for hazardous purposes has increased recently. To prevent those illegal drones, in this work, we propose a novel framework for reconstructing 3D trajectories of drones using a single camera. By leveraging calibrated cameras, we exploit the relationship between 2D and 3D spaces. We automatically track the drones in 2D images using the drone tracker and estimate their 2D rotations. By combining the estimated 2D drone positions with their actual length information and camera parameters, we geometrically infer the 3D trajectories of the drones. To address the lack of public drone datasets, we al…

2. Apparenté Pas pertinent

   A General Pipeline for 3D Detection of Vehicles

   Xinxin Du, Marcelo H. Ang Jr., Sertac Karaman et al. · 2018 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D detection networks. To identify the 3D box, an effective model fitting algorithm is developed based on generalised car models and score maps. A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks. The 3D detection results based on these two networks are simi…

3. Apparenté Pas pertinent

   Fast Vehicle Detection in Aerial Imagery

   Jennifer Carlet, Bernard Abayowa · 2017 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   In recent years, several real-time or near real-time object detectors have been developed. However these object detectors are typically designed for first-person view images where the subject is large in the image and do not directly apply well to detecting vehicles in aerial imagery. Though some detectors have been developed for aerial imagery, these are either slow or do not handle multi-scale imagery very well. Here the popular YOLOv2 detector is modified to vastly improve it's performance on aerial data. The modified detector is compared to Faster RCNN on several aerial imagery datasets. The proposed detector gives near state of the art performance at more than 4x the speed.

4. Apparenté Pas pertinent

   Learning Controls Using Cross-Modal Representations: Bridging Simulation and Reality for Drone Racing

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   Résumé scientifique

   Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) drone racing. While recent advances in Machine Learning, especially Reinforcement and Imitation Learning show promise, they are constrained by the need of large amounts of difficult to collect real-world data for learning robust behaviors in diverse scenarios. In this work we propose to learn rich representations and policies by leveraging unsupervised data, such as video footage from an FPV drone, together with easy to generate simulated labeled data. Our approach takes a cross-modal perspective, where separate modalities correspond to the raw camera sensor data and the system …

5. Apparenté Pas pertinent

   Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2

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   Résumé scientifique

   Detection of pedestrians in aerial imagery captured by drones has many applications including intersection monitoring, patrolling, and surveillance, to name a few. However, the problem is involved due to continuouslychanging camera viewpoint and object appearance as well as the need for lightweight algorithms to run on on-board embedded systems. To address this issue, the paper proposes a framework for pedestrian detection in videos based on the YOLO object detection network [6] while having a high throughput of more than 5 FPS on the Jetson TX2 embedded board. The framework exploits deep learning for robust operation and uses a pre-trained model without the need for any additional training …

6. Apparenté Pas pertinent

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   Résumé scientifique

   Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes…

7. Apparenté Pas pertinent

   Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing

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   Résumé scientifique

   Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time. This paper proposes a novel baseline architecture for developing sophisticated models capable of true hardware-enabled parallelism, achieving neural processing speeds that mirror the agent's high velocity. The proposed model (Parallel Perception Network (PPN)) consists of two independent neural networks, segmentation and reconstruction n…

8. Apparenté Pas pertinent

   RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety

   Marco Wallner, Daniel Steininger, Verena Widhalm et al. · 2021 · Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 1266 · Résumé IA disponible

   Résumé scientifique

   Automated monitoring and analysis of passenger movement in safety-critical parts of transport infrastructures represent a relevant visual surveillance task. Recent breakthroughs in visual representation learning and spatial sensing opened up new possibilities for detecting and tracking humans and objects within a 3D spatial context. This paper proposes a flexible analysis scheme and a thorough evaluation of various processing pipelines to detect and track humans on a ground plane, calibrated automatically via stereo depth and pedestrian detection. We consider multiple combinations within a set of RGB- and depth-based detection and tracking modalities. We exploit the modular concepts of Meshr…