Abstract
As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
| Original language | English |
|---|---|
| Article number | 133 |
| Journal | Eurasip Journal on Image and Video Processing |
| Volume | 2018 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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Keywords
- Automobile tracking system
- Computer vision
- Database
- Index structure
- Real-time
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Electrical and Electronic Engineering
Cite this
Real-time car tracking system based on surveillance videos. / Jung, Seungwon; Kim, Yongsung; Hwang, Een Jun.
In: Eurasip Journal on Image and Video Processing, Vol. 2018, No. 1, 133, 01.12.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Real-time car tracking system based on surveillance videos
AU - Jung, Seungwon
AU - Kim, Yongsung
AU - Hwang, Een Jun
PY - 2018/12/1
Y1 - 2018/12/1
N2 - As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
AB - As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
KW - Automobile tracking system
KW - Computer vision
KW - Database
KW - Index structure
KW - Real-time
UR - http://www.scopus.com/inward/record.url?scp=85057822263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057822263&partnerID=8YFLogxK
U2 - 10.1186/s13640-018-0374-7
DO - 10.1186/s13640-018-0374-7
M3 - Article
AN - SCOPUS:85057822263
VL - 2018
JO - Eurasip Journal on Image and Video Processing
JF - Eurasip Journal on Image and Video Processing
SN - 1687-5176
IS - 1
M1 - 133
ER -