A motion detection model inspired by hippocampal function and its applications to obstacle detection

2014 ◽  
Vol 129 ◽  
pp. 59-66 ◽  
Author(s):  
Haichao Liang ◽  
Takashi Morie
Keyword(s):  
Motion Detection ◽  
Obstacle Detection ◽  
Hippocampal Function ◽  
Detection Model

scholarly journals A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 683 ◽  
Author(s):  
Gonzalo Farias ◽  
Ernesto Fabregas ◽  
Emmanuel Peralta ◽  
Héctor Vargas ◽  
Gabriel Hermosilla ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstacle Detection ◽  
Network Approach ◽  
Neural Network Approach ◽  
Detection Model

A scene-adaptive motion detection model based on machine learning and data clustering

2013 ◽  
Vol 74 (8) ◽  
pp. 2821-2839 ◽  
Author(s):  
Tao Hu ◽  
Minghui Zheng ◽  
Jun Li ◽  
Li Zhu ◽  
Jia Hu
Keyword(s):  
Machine Learning ◽  
Motion Detection ◽  
Data Clustering ◽  
Detection Model ◽  
Model Based ◽  
Adaptive Motion

scholarly journals A 2-D Motion Detection Model for Low-Cost Embedded Reconfigurable I/O Devices

2005 ◽  
Vol 52 (8) ◽  
pp. 1443-1449 ◽  
Author(s):  
A. Dollas ◽  
S. Sotiropoulos ◽  
K. Papademetriou
Keyword(s):  
Motion Detection ◽  
Low Cost ◽  
Detection Model

scholarly journals Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Feng ◽  
Ronghui Yan ◽  
Guangping Liu ◽  
Chen Shao
Keyword(s):  
Situation Awareness ◽  
Network Models ◽  
Obstacle Detection ◽  
Detection Accuracy ◽  
Cloud Platform ◽  
Detection Model ◽  
Identification Method ◽  
Detection And Identification ◽  
Subway Train ◽  
Edge Side

The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real-time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature-level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.

scholarly journals A motion detection model based on a recurrent network

2011 ◽  
Vol 11 (11) ◽  
pp. 746-746
Author(s):  
J. Joukes ◽  
B. Krekelberg
Keyword(s):  
Motion Detection ◽  
Recurrent Network ◽  
Detection Model ◽  
Model Based
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