基于历史信息的区域卷积神经网络行人检测

Continuous pedestrian detection by means of regional convolutional neural network based on historical information

为了解决卷积神经网络在进行连续行人检测时,检测行人速度较慢,达不到实时性要求的问题,采用基于历史信息的区域卷积神经网络行人检测算法,利用前一幅图像中的检测结果对当前图像的检测过程进行优化,将前一帧的检测结果作为对当前帧提取推荐区域的参考信息,并使用当前帧与前一帧的灰度值差异图对当前图像的卷积特征进行过滤,以缩小滑动窗口检测时的搜索区域。在加州理工学院行人检测数据集上进行了检测实验。结果表明,结合历史信息的算法与先进的算法相比检测速度提升了2.5倍,同时检测准确率提升了1.5%。该算法实现了实时行人检测,设计的网络能有效检测小目标行人。

In order to solve the problem that convolutional neural network detection of pedestrians was slow, and did not meet the real-time requirement when performing continuous pedestrian detection, pedestrian detection algorithm of history-based region with convolutional neural network was used. Current image was detected by using the detection result in the previous image. The detection process was optimized, and the detection result of the previous image was used as reference information for extracting region proposals of the current image. Convolution feature of the current image was filtered by using the gray value difference map of the current image and the previous image to reduce the sliding window searching area. The results of Caltech pedestrian detection data set show that the algorithm combined with historical information is 2.5 times faster than the advanced algorithm, and the detection accuracy is increased by 1.5%. The algorithm implements real-time pedestrian detection, and the designed network can effectively detect small target pedestrians.