Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras
Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/7/963 |
| _version_ | 1828137314794078208 |
|---|---|
| author | Jaehoon Jung Inhye Yoon Seungwon Lee Joonki Paik |
| author_facet | Jaehoon Jung Inhye Yoon Seungwon Lee Joonki Paik |
| author_sort | Jaehoon Jung |
| collection | DOAJ |
| description | Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system. |
| first_indexed | 2024-04-11T18:16:31Z |
| format | Article |
| id | doaj.art-99b0cd159496483fb5c51a9369389889 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:16:31Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99b0cd159496483fb5c51a93693898892022-12-22T04:09:52ZengMDPI AGSensors1424-82202016-06-0116796310.3390/s16070963s16070963Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance CamerasJaehoon Jung0Inhye Yoon1Seungwon Lee2Joonki Paik3Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaDepartment of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaDepartment of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaDepartment of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaSince it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system.http://www.mdpi.com/1424-8220/16/7/963video surveillancevideo retrievalautomatic calibrationmetadata descriptorhomologycolor clusteringobject tracking |
| spellingShingle | Jaehoon Jung Inhye Yoon Seungwon Lee Joonki Paik Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras Sensors video surveillance video retrieval automatic calibration metadata descriptor homology color clustering object tracking |
| title | Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras |
| title_full | Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras |
| title_fullStr | Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras |
| title_full_unstemmed | Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras |
| title_short | Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras |
| title_sort | normalized metadata generation for human retrieval using multiple video surveillance cameras |
| topic | video surveillance video retrieval automatic calibration metadata descriptor homology color clustering object tracking |
| url | http://www.mdpi.com/1424-8220/16/7/963 |
| work_keys_str_mv | AT jaehoonjung normalizedmetadatagenerationforhumanretrievalusingmultiplevideosurveillancecameras AT inhyeyoon normalizedmetadatagenerationforhumanretrievalusingmultiplevideosurveillancecameras AT seungwonlee normalizedmetadatagenerationforhumanretrievalusingmultiplevideosurveillancecameras AT joonkipaik normalizedmetadatagenerationforhumanretrievalusingmultiplevideosurveillancecameras |