IEICE TRANSACTIONS on Information
Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View
Summary :
A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.11 pp.2221-2229
- Publication Date
- 2019/11/01
- Publicized
- 2019/07/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7305
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Houari SABIRIN
KDDI Research, Inc.
Hitoshi NISHIMURA
KDDI Research, Inc.
Sei NAITO
KDDI Research, Inc.
Keyword
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Houari SABIRIN, Hitoshi NISHIMURA, Sei NAITO, "Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2221-2229, November 2019, doi: 10.1587/transinf.2018EDP7305.
Abstract: A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7305/_p
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@ARTICLE{e102-d_11_2221,
author={Houari SABIRIN, Hitoshi NISHIMURA, Sei NAITO, },
journal={IEICE TRANSACTIONS on Information},
title={Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View},
year={2019},
volume={E102-D},
number={11},
pages={2221-2229},
abstract={A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.},
keywords={},
doi={10.1587/transinf.2018EDP7305},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View
T2 - IEICE TRANSACTIONS on Information
SP - 2221
EP - 2229
AU - Houari SABIRIN
AU - Hitoshi NISHIMURA
AU - Sei NAITO
PY - 2019
DO - 10.1587/transinf.2018EDP7305
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.
ER -
English
