Tracking The Invisible Man - Hidden-object Detection for Complex Visual Scene Understanding

2016 ◽  
Author(s):  
Joanna Isabelle Olszewska
Keyword(s):  
Object Detection ◽  
Scene Understanding ◽  
Visual Scene ◽  
Invisible Man ◽  
Visual Scene Understanding ◽  
The Invisible

Neuroscience-Enabled Complex Visual Scene Understanding

2012 ◽  
Author(s):  
Laurent Itti ◽  
Nader Noori ◽  
Lior Elazary
Keyword(s):  
Scene Understanding ◽  
Visual Scene ◽  
Visual Scene Understanding

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

2019 ◽  
pp. 285-296 ◽  
Author(s):  
Markus Hofmarcher ◽  
Thomas Unterthiner ◽  
José Arjona-Medina ◽  
Günter Klambauer ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Scene Understanding ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Visual Scene ◽  
Visual Scene Understanding

scholarly journals A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yuezhong Wu ◽  
Xuehao Shen ◽  
Qiang Liu ◽  
Falong Xiao ◽  
Changyun Li
Keyword(s):  
Real Time ◽  
Scene Understanding ◽  
Cloud Service ◽  
Heterogeneous Data ◽  
Visual Scene ◽  
Classification Method ◽  
Service Robots ◽  
Service Robot ◽  
Server Side ◽  
Visual Scene Understanding

Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.

Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes

2013 ◽  
Vol 35 (4) ◽  
pp. 882-897 ◽  
Author(s):  
Christian Wojek ◽  
Stefan Walk ◽  
Stefan Roth ◽  
Konrad Schindler ◽  
Bernt Schiele
Keyword(s):  
Scene Understanding ◽  
Visual Scene ◽  
Visual Scene Understanding

scholarly journals Enhanced visual scene understanding through human-robot dialog

2011 ◽  
Author(s):  
Matthew Johnson-Roberson ◽  
Jeannette Bohg ◽  
Gabriel Skantze ◽  
Joakim Gustafson ◽  
Rolf Carlson ◽  
...  
Keyword(s):  
Scene Understanding ◽  
Visual Scene ◽  
Visual Scene Understanding

scholarly journals Enhanced visual scene understanding through human-robot dialog

2011 ◽  
Author(s):  
Matthew Johnson-Roberson ◽  
Jeannette Bohg ◽  
Gabriel Skantze ◽  
Joakim Gustafson ◽  
Rolf Carlson ◽  
...  
Keyword(s):  
Scene Understanding ◽  
Visual Scene ◽  
Visual Scene Understanding

Visual Scene Understanding with Contexts

2018 ◽  
Vol 45 (12) ◽  
pp. 1279-1286
Author(s):  
Donghyeop Shin ◽  
Incheol Kim
Keyword(s):  
Scene Understanding ◽  
Visual Scene ◽  
Visual Scene Understanding
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