A New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis
A New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis
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DOI：
10.1109/aike48582.2020.00012
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Hadi Ghahremannezhad;Hang Shi;Chengjun Liu
中科院分区：
文献类型：
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作者：
Hadi Ghahremannezhad;Hang Shi;Chengjun Liu
基于实时智能视频的交通监控应用在智能交通系统中发挥着重要作用。为了减少误报并提高计算效率，用于自动感兴趣区域（RoI）检测的鲁棒道路分割成为研究界的热门焦点。提出了一种新的自适应双向检测感兴趣区域方法，用于自动将双向交通流的道路分割成两个感兴趣区域。具体地，首先应用前景分割方法沿着洪水填充算法来估计道路区域。然后利用Lucas-Kanade光流算法对道路进行实时跟踪并划分出感兴趣区域。使用真实的交通视频数据集的实验结果说明了所提出的方法用于实时自动确定ROI的可行性。
Real-time intelligent video-based traffic surveillance applications play an important role in intelligent transportation systems. To reduce false alarms as well as to increase computational efficiency, robust road segmentation for automated Region of Interest (RoI) detection becomes a popular focus in the research community. A novel Adaptive Bidirectional Detection (ABD) of region-of-interest method is presented in this paper to automatically segment the roads with bidirectional traffic flows into two regions of interest. Specifically, a foreground segmentation method is first applied along with the flood-fill algorithm to estimate the road regions. Then the Lucas-Kanade’s optical flow algorithm is utilized to track and divide the estimated road into regions of interest in real-time. Experimental results using a dataset of real traffic videos illustrate the feasibility of the proposed method for automatically determining the RoIs in real-time.