Web mining driven semantic scene understanding and object localization

2011 ◽  
Author(s):  
Kai Zhou ◽  
Karthik Mahesh Varadarajan ◽  
Michael Zillich ◽  
Markus Vincze
Keyword(s):  
Web Mining ◽  
Scene Understanding ◽  
Object Localization ◽  
Semantic Scene

scholarly journals MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding

2020 ◽  
Vol 169 ◽  
pp. 337-350
Author(s):  
Xiaoman Qi ◽  
Panpan Zhu ◽  
Yuebin Wang ◽  
Liqiang Zhang ◽  
Junhuan Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Scene Understanding ◽  
Semantic Scene

scholarly journals PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

2019 ◽  
Vol 9 (18) ◽  
pp. 3789 ◽  
Author(s):  
Jiyoun Moon ◽  
Beom-Hee Lee
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Scene Understanding ◽  
Semantic Knowledge ◽  
Ground Vehicles ◽  
Learning Techniques ◽  
Recent Developments ◽  
Heterogeneous Robots ◽  
Semantic Scene

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.

scholarly journals SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 637-644
Author(s):  
B. Vishnyakov ◽  
Y. Blokhinov ◽  
I. Sgibnev ◽  
V. Sheverdin ◽  
A. Sorokin ◽  
...  
Keyword(s):  
Data Collection ◽  
Autonomous Vehicles ◽  
Three Dimensional ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Lidar Data ◽  
Point Cloud Segmentation ◽  
Detection Point ◽  
Sensor Platform ◽  
Semantic Scene

Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.

scholarly journals SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

2019 ◽  
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Scene Understanding ◽  
Semantic Scene

Indoor Semantic Scene Understanding Using 2D-3D Fusion

2021 ◽  
Author(s):  
Muraleekrishna Gopinathan ◽  
Giang Truong ◽  
Jumana Abu-Khalaf
Keyword(s):  
Scene Understanding ◽  
Semantic Scene

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

2021 ◽  
pp. 027836492110067
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Point Cloud ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lessons Learned ◽  
Multiple Point ◽  
3D Point Cloud ◽  
Core Capability ◽  
Semantic Scene ◽  
3D Lidar

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

GEOMETRIC INVARIANTS CONSTRUCTION FOR SEMANTIC SCENE UNDERSTANDING FROM MULTIPLE VIEWS INSPIRED BY THE HUMAN VISUAL SYSTEM

2012 ◽  
Vol 12 (02) ◽  
pp. 1250012 ◽  
Author(s):  
N. FAN ◽  
CHENG JIN
Keyword(s):  
Belief Propagation ◽  
Scene Understanding ◽  
Optimal Solution ◽  
Multiple Views ◽  
Geometric Invariants ◽  
Pairwise Interactions ◽  
Semantic Scene ◽  
Segmentation Task ◽  
Class Segmentation ◽  
Segmentation Problem

Semantic scene understanding is one of the several significant goals of robotics. In this paper, we propose a framework that is able to construct geometric invariants for simultaneous object detection and segmentation using a simple pairwise interactive context term, for the sake of achieving a preliminary milestone of Semantic scene understanding. The context is incorporated as pairwise interactions between pixels, imposing a prior on the labeling. Our model formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution using belief propagation and neural network. We experimentally evaluate the proposed method on three publicly available datasets: the MSRC-1, the CorelB datasets, and the PASCAL VOC database. Results show the applicability and efficacy of the proposed method to the multi-class segmentation problem.

Realization of Scenarios for Video Surveillance

2012 ◽  
Vol 17 (4) ◽  
pp. 231-240 ◽  
Author(s):  
Wojciech Chmiel ◽  
Joanna Kwiecień ◽  
Zbigniew Mikrut
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Video Surveillance ◽  
Scene Understanding ◽  
Object Localization ◽  
Movement Detection

Abstract The design of a methodology for the effective scene understanding systems is one of the main goals of the researchers in the analysis of video surveillance. The objects in the scene have to be identified. Hence, it is necessary to detect the parts belonging to the background. In the article we introduce the base algorithms, which enable us to realization of scenarios. We briefly describe base algorithms (object detection, object localization, recognition of humans, movement detection and configuration of scene) used in three selected scenarios: violation of protected zones, abandoned objects and vandalism (graffiti). These scenarios were tested on several films, obtained from Internet and made by participants of project SIMPOZ. The results of our experiments are presented. The basic algorithms for detecting and locating objects are very quickly, but movement detection ("optical flow") and recognition of humans algorithms work longer.

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