Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Objective: The objective of this study is to develop a novel algorithm on a mobile system that can warn drivers about the possibility of a collision with a pedestrian. The constraints of the algorithm are near-real-time detection speed and a good detection rate.

Method: Histogram of gradients (HOG)-based detection is widely used in pedestrian safety applications; however, it has low detection speed for real-time systems. Hence, it has no direct usage for mobile systems. In order to achieve near-real-time detection speed, partial Haar transform predetections are applied to an image before HOG detection. The partial and HOG detections are merged and a score-based confidence level is defined for the final detection phase. In this way, the outcome is prioritized and different warning levels can be issued to warn the driver before a possible pedestrian collision.

Results: The proposed algorithm provides an increase in detection speed (from 46 to 76 fps) and detection rate (from 80 to 91%) with respect to HOG-based pedestrian detection. It also improves confidence of the results by multidetection merging and score assignment to detections.

Conclusions: Performance improvement of the algorithm is compared with respect to state-of-the-art detectors/algorithms. Based on the detection rate and detection speed performance, it can be concluded that the proposed algorithm is suitable to be used for mobile systems to warn drivers about the possibility of collision with a pedestrian.     

一种HSI空间基于模糊聚类的火灾彩色图像分割算法

针对通用模糊聚类算法进行彩色图像分割存在对初值敏感,迭代过程耗时等问题,在HSI空间结合火焰图像分布特征,采用平均值法进行初值优选,构造抑制算子和抑制因数对火焰无关区域S和I分量进行有效抑制,采用直方图聚类后进行数据融合等方式,最终实现彩色火灾图像分割。实验表明,该算法提高了彩色火灾图像分割的准确性和收敛速度。     

重庆雾水

本文系统地叙述了雾水的采集与分析,统计分析了由于重庆大气污染造成雾水组分及时空分布的差异,对发展趋势也进行了分析.