Modified RPCA with Hessian matrix for object detection in video surveillance on highways

2014 Sixth International Conference on Advanced Computing (ICoAC) Pub Date : 2014-12-01 DOI:10.1109/ICOAC.2014.7229719

K. Kiruba, P. Sathiya, P. Anandhakumar

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引用次数: 1

Abstract

Video surveillance is an active research topic in computer vision research area to detect the abnormal behavior of vehicle and pedestrian on the highways in order to reduce the collision between them. Statistical methods are helpful in identifying the abnormal behavior of vehicle and human in order to avoid the accident on the highways. To build an effective automatic system that should determine the number of pedestrian and vehicles, if there are any, then their distance and speed needs to be calculated. Detecting object and calculating their speed and distance is challenging task because objects are moving fast on highways, and appear at different scales. In this paper, we propose a Modified RPCA with Hessian matrix for vehicle and pedestrian detection. By using an SVM classifier, it will be able to classify the objects in the current frame. Distance is calculated between the vehicle and pedestrian, speed and their locations. If the distance value is below the defined coverage (50 meters) their performance is evaluated and compared between RPCA and Modified RPCA. The modified RPCA is more efficient than RPCA.

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基于Hessian矩阵的改进RPCA高速公路视频监控目标检测

视频监控是计算机视觉研究领域中一个活跃的研究课题，目的是检测高速公路上车辆和行人的异常行为，以减少车辆和行人之间的碰撞。统计方法有助于识别车辆和人的异常行为，从而避免高速公路上的事故发生。为了建立一个有效的自动系统，它应该确定行人和车辆的数量，如果有的话，那么它们的距离和速度需要计算。检测物体并计算它们的速度和距离是一项具有挑战性的任务，因为物体在高速公路上快速移动，并且出现在不同的尺度上。本文提出了一种基于Hessian矩阵的改进RPCA算法，用于车辆和行人的检测。通过使用SVM分类器，它将能够对当前帧中的对象进行分类。计算车辆与行人之间的距离，速度和他们的位置。如果距离值低于定义的覆盖范围(50米)，则评估RPCA和改进RPCA的性能并进行比较。改进后的RPCA比RPCA效率更高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 Sixth International Conference on Advanced Computing (ICoAC)

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