Moving Vehicle Identification Using Background Registration Technique for Traffic Surveillance (original) (raw)
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An innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented. This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion.
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Traffic data collection is an essential issue for road-traffic control departments, which need real-time information for traffic-parameter estimation: road-traffic intensity, lane occupancy, congestion level, estimation of journey times, etc., as well as for early incident detection. This information can be used to improve road safety as well as to make an optimal use of the existing infrastructure or to estimate new infrastructure needs. In an intelligent transportation system, traffic data may come from different kinds of sensors. The use of video cameras (many of which are already installed to survey road networks), coupled with computer vision techniques, offers an attractive alternative to other traffic sensors (Michalopoulos, 1991). For instance, they can provide powerful processing capabilities for vehicle tracking and classification, providing a non-invasive and easier to install alternative to traditional loop detectors (Fathy & Siyal, 1998; Ha et al., 2004). Successful video-based systems for urban traffic monitoring must be adaptive to different traffic or environmental conditions (Zhu & Xu, 2000; Zhou et al., 2007). Key aspects to be considered are motion-based foreground/background segmentation (
Transport and Telecommunication Journal
The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.