Neural controller for PTZ cameras based on nonpanoramic foreground detection

Molina‐Cabello, Miguel A.; López‐Rubio, Ezequiel; Luque‐Baena, Rafael Marcos; Domínguez, Enrique; Thurnhofer‐Hemsi, Karl

doi:10.1109/ijcnn.2017.7965882

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…"#$%( For solving this problem, there exist two main approaches. One is to utilize a background subtraction algorithm as preprocessing for extracting the target data and generating the topological structure of only the target object [45,46]. The other is to apply a weighted distance measurement in the winner node selection according to the importance of each property [47,48].…”

Section: Introductionmentioning

confidence: 99%

Growing Neural Gas with Different Topologies for 3D Space Perception

et al. 2022

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Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research.

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Section: Introductionmentioning

confidence: 99%

Growing Neural Gas with Different Topologies for 3D Space Perception

et al. 2022

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Topological based Environmental Reconstruction for Efficient Multi-Level Control of Robot Locomotion

Saputra

Hong

Yani

et al. 2022

2022 International Electronics Symposium (IES)

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Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

Ardilla

Saputra

Kubota

2023

IJAT

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Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.

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Neural controller for PTZ cameras based on nonpanoramic foreground detection

Cited by 4 publications

References 13 publications

Growing Neural Gas with Different Topologies for 3D Space Perception

Growing Neural Gas with Different Topologies for 3D Space Perception

Topological based Environmental Reconstruction for Efficient Multi-Level Control of Robot Locomotion

Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

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