Abstract

Existing smoky vehicle detection methods are vulnerable to false alarms because of the continuous interferences from common passed vehicles and the complex characteristics of smoke. This paper presents a video smoky vehicle detection method based on dynamic features. Three groups of features, including Multi-Sequence Integral Projection (MS-IP), Center-Symmetric Local Binary Patterns on Three Orthogonal Planes (CSLBP-TOP) and Histograms of Oriented Optical Flow (HOOF), are proposed or employed to characterize dynamic features of successive Region of Interest (ROIs). More specifically, the MS-IP characterizes the diffusion and distribution information based on multiple-sequence analysis and integral projection. The CSLBP-TOP characterizes the spatiotemporal texture information by (1) combining the strengths of Shift-Invariant Feature Transform (SIFT) and LBP and (2) extending the spatial features to three-dimensional (3D) space based on three orthogonal planes (TOP). The HOOF characterizes the motion information by inducing a very characteristic optical flow profile to distinguish smoky objects and non-smoky objects in successive ROIs based on the fact that the smoke is ejected from vehicle exhaust port and then gradually spreads around. The above three groups of features are complementary, and we fuse them to increase algorithm robustness. Experiment results show that our method achieves better performances than existing methods.

中文翻译：

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基于动态功能从交通监控视频中检测黑烟车辆

摘要

由于常见的经过车辆的持续干扰和烟雾的复杂特性，现有的黑烟车辆检测方法容易受到虚假警报的影响。本文提出了一种基于动态特征的视频黑烟车辆检测方法。提出或采用三组特征来表征动态特征，包括多序列整体投影（MS-IP），三个正交平面上的中心对称局部二进位图（CSLBP-TOP）和定向光流直方图（HOOF）。连续关注区域（ROI）的数量。更具体地说，MS-IP基于多序列分析和整体投影来表征扩散和分布信息。CSLBP-TOP通过（1）结合Shift-不变特征变换（SIFT）和LBP的优势以及（2）基于三个正交平面（TOP）将空间特征扩展到三维（3D）空间来表征时空纹理信息）。HOOF基于烟雾从车辆排气口喷出然后逐渐扩散的事实，通过诱导非常特征性的光流轮廓来区分连续ROI中的烟熏物和非烟熏物，来表征运动信息。以上三组功能是互补的，我们将它们融合以提高算法的鲁棒性。实验结果表明，该方法具有比现有方法更好的性能。HOOF基于烟雾从车辆排气口喷出然后逐渐扩散的事实，通过诱导非常特征性的光流轮廓来区分连续ROI中的烟熏物和非烟熏物，来表征运动信息。以上三组功能是互补的，我们将它们融合以提高算法的鲁棒性。实验结果表明，该方法具有比现有方法更好的性能。HOOF基于烟雾从车辆排气口喷出然后逐渐扩散的事实，通过诱导非常有特色的光流轮廓来区分连续ROI中的烟熏物和非烟熏物，来表征运动信息。以上三组功能是互补的，我们将它们融合以提高算法的鲁棒性。实验结果表明，该方法具有比现有方法更好的性能。