High-Level Semantic Feature Detector: Pedestrian Detection Based On Improved Mask R-CNN Algorithm

Abstract

Pedestrian detection is a crucial topic that must be solved for a variety of reasons, including its usefulness in the domains of advanced mechanics, car safety, and surveillance. A significant portion of the progress over the last several years has been driven by the ease with which people may test their hypotheses on publicly available datasets and offer forth workable answers. In the current day, as deep learning approaches, it is common practise to use sliding-window classifiers in bespoke or anchor-based expectations for object recognition. To keep up with the rapid pace of development, this paper presents a new perspective in which object recognition is conceived of as a semantic object detection task at an undeniable level, and it also introduces refined evaluation metrics that demonstrate how commonly used per-window measures are ineffective and can fail to predict performance on full images. The proposed hybrid model combines the strengths of MobileNet and ResNet50's skip connection with Faster RCNN to analyse the whole image and pull out the relevant characteristics for detection. However, the suggested approach departs from the standard, low-level provisioning and instead focuses on a higher-level detection. Therefore, this research simplifies pedestrian identification using convolutions, reducing it to a focus and scale expectation job. As a result, the suggested method is straightforward, offers competitive accuracy and remarkable speed, and inspires the development of a novel, appealing pedestrian detector.