Occlusion Vehicle Segmentation Algorithm in Crowded Scene for Traffic Surveillance System
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Traffic surveillance system (TSS) is an essential tool to extract necessary information (count, type, speed, etc.) from cameras for further analysis. In this issue, vehicle detection is considered one of the most important studies as it is a vital process from which modules such as vehicle tracking and classification can be built upon. However, detecting moving vehicles in urban areas is difficult because the inter-vehicle space is significantly reduced, which increases the occlusion among vehicles. This issue is more challenging in developing countries where the roads are crowded with 2-wheeled motorbikes in rush hours. This paper proposes a method to improve the occlusion vehicle detection from static surveillance cameras. The main contribution is an overlapping vehicle segmentation algorithm in which undefined blobs of occluded vehicles are examined to extract the vehicles individually based on the geometry and the ellipticity characteristics of objects’ shapes. Experiments on real-world data have shown promising results with a detection rate of 84.10% in daytime scenes.
KeywordsOcclusion detection Blob splitting Vehicle segmentation Traffic surveillance system
This research is funded by International University—Vietnam National University Ho Chi Minh City (VNU-HCM) under Grant Number SV2016-IT-05.
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