Abstract
Although pedestrian detection technology is constantly improving, pedestrian detection remains challenging because of the uncertainty and diversity of pedestrians in different scales and of occluded pedestrian modes. This study followed the common framework of single-shot object detection and proposed a divide-and-rule method to solve the aforementioned problems. The proposed model introduced a segmentation function that can split pedestrians who do not overlap in one image into two subimages. By using a network architecture, multiresolution adaptive fusion was performed on the output of all images and subimages to generate the final detection result. This study conducted an extensive evaluation of several challenging pedestrian detection data sets and finally proved the effectiveness of the proposed model. In particular, the proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012 (VOC 2012), the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.
Highlights
Images of pedestrians at different scales, occlusions, and special aspect ratios often occur in practical applications, and this is a massive challenge for pedestrian detection
To resolve the problems of uncertainty and diversity of pedestrians in special aspect ratio and of occluded pedestrian modes, this study used a single-shot object detector, You only look once (YOLO) v4 [13], as a base and initially imported the image length and width information into the network to alleviate the problem of image distortion after resizing
The experimental results indicated that the proposed model provides excellent detection results in popular pedestrian data sets and manually and horizontally expands triple-length images in this study
Summary
Images of pedestrians at different scales, occlusions, and special aspect ratios often occur in practical applications, and this is a massive challenge for pedestrian detection. To resolve the problems of uncertainty and diversity of pedestrians in special aspect ratio and of occluded pedestrian modes, this study used a single-shot object detector, You only look once (YOLO) v4 [13], as a base and initially imported the image length and width information into the network to alleviate the problem of image distortion after resizing. Because detecting small-scale pedestrians and occlusion problems in an image is difficult, most deep learning involves multiscale modeling to improve the detection results, including Faster R-CNN [14], Yolov3 [15], and SSD [16] These algorithms achieve performance growth after multiscale modeling is added, this problem cannot be solved when encountering images with special aspect ratios, which cause subsequent detection problems.
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