An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks

Automated motion detection technology is an integral component of intelligent transportation systems, and is particularly essential for management of traffic and maintenance of traffic surveillance systems. Traffic surveillance systems using video communication over real-world networks with limited...

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Bibliographic Details
Published in:IEEE transactions on multimedia Vol. 16; no. 3; pp. 837 - 847
Main Authors: Chen, Bo-Hao, Huang, Shih-Chia
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Artificial intelligence
Bandwidth
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Ground, air and sea transportation, marine construction
Intelligent transportation systems
Motion detection
Motion perception
moving object detection
Networks
neural network
Object detection
Pattern recognition. Digital image processing. Computational geometry
principal component analysis
Radial basis function networks
Radiocommunications
Road transportation and traffic
Software
Streaming media
Streams
Surveillance
Surveillance systems
Telecommunications
Telecommunications and information theory
Traffic congestion
Traffic engineering
Traffic flow
variable bit-rate
Video communication
Wireless communication
Video signal
Modeling
Adaptive method
Intelligent transportation systems
Bandwidth
Motion detection
Low bit rate
Monitoring
Quantitative analysis
Streaming
Computer vision
Motion estimation
Maintenance
Neural network
Bandwidth allocation
Communication network
Real time
Variable bit rate
variable bit-rate
Radial basis function
Surveillance
Automatic measurement
Videocommunication
Traffic management
Object detection
moving object detection
Wireless network
Traffic congestion
Moving body
Principal component analysis
ISSN:1520-9210, 1941-0077
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Description
Summary:Automated motion detection technology is an integral component of intelligent transportation systems, and is particularly essential for management of traffic and maintenance of traffic surveillance systems. Traffic surveillance systems using video communication over real-world networks with limited bandwidth often encounter difficulties due to network congestion and/or unstable bandwidth. This is especially problematic in wireless video communication. This has necessitated the development of a rate control scheme which alters the bit-rate to match the obtainable network bandwidth, thereby producing variable bit-rate video streams. However, complete and accurate detection of moving objects under variable bit-rate video streams is a very difficult task. In this paper, we propose an approach for motion detection which utilizes an analysis-based radial basis function network as its principal component. This approach is applicable not only in high bit-rate video streams, but in low bit-rate video streams, as well. The proposed approach consists of a various background generation stage and a moving object detection stage. During the various background generation stage, the lower-dimensional Eigen-patterns and the adaptive background model are established in variable bit-rate video streams by using the proposed approach in order to accommodate the properties of variable bit-rate video streams. During the moving object detection stage, moving objects are extracted via the proposed approach in both low bit-rate and high bit-rate video streams; detection results are then generated through the output value of the proposed approach. The detection results produced through our approach indicate it to be highly effective in variable bit-rate video streams over real-world limited bandwidth networks. In addition, the proposed method can be easily achieved for real-time application. Quantitative and qualitative evaluations demonstrate that it offers advantages over other state-of-the-art methods. For instance, Similarity and F 1 accuracy rates produced via the proposed approach were up to 86.38% and 89.88% higher than those produced via other compared methods, respectively.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2014.2298377