a Department of System Engineering, Tübitak Iltaren , Ankara , Turkey.
b Department of Electrical & Electronics Engineering, Faculty of Engineering, Hacettepe University , Ankara , Turkey.
Traffic Inj Prev. 2019;20(6):619-623. doi: 10.1080/15389588.2019.1624731. Epub 2019 Jun 27.
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. 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. 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. 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.
本研究的目的是开发一种可用于移动系统的新型算法，以便在驾驶员可能与行人发生碰撞时发出警告。该算法的约束条件是接近实时的检测速度和良好的检测率。基于梯度直方图（HOG）的检测在行人安全应用中得到了广泛应用，但对于实时系统，其检测速度较低。因此，它不直接适用于移动系统。为了实现接近实时的检测速度，在进行 HOG 检测之前，将部分 Haar 变换预测应用于图像。对部分和 HOG 检测进行合并，并为最终的检测阶段定义基于分数的置信度水平。通过这种方式，对结果进行优先级排序，并在可能发生行人碰撞之前发出不同的警告级别，以警告驾驶员。与基于 HOG 的行人检测相比，所提出的算法提高了检测速度（从 46 到 76 fps）和检测率（从 80 到 91%）。它还通过多检测合并和分数分配来提高检测结果的置信度。算法的性能改进与最先进的检测器/算法进行了比较。根据检测率和检测速度的性能，可以得出结论，所提出的算法适用于移动系统，以便警告驾驶员可能与行人发生碰撞。
