Fast and Robust People Detection in RGB Images

Abstract

People detection in images has many uses today, ranging from face detection algorithms used by social networks to help the users tag other people, to surveillance systems that can create a statistic of the population density in an area, or identify a suspect, or even in the automotive industry as part of the Pedestrian Crash Avoidance Mitigation (PCAM) system. This work focuses on creating a fast and reliable object detection algorithm that will be trained on scenes that depict people in an indoor environment, starting from an existing state-of-the-art approach. The proposed method improves upon the You Only Look Once version 4 (YOLOv4) network by adding a region of interest classification and regression branch such as Faster R-CNN’s head. The candidate bounding boxes proposed by YOLOv4 are ranked based on their confidence score, the best candidates being kept and sent as input to the Faster Region-Based Convolutional Neural Network (R-CNN) head. To keep only the best detections, non-maximum suppression is applied to all proposals. This decreases the number of false-positive candidate bounding boxes, the low-confidence detections of the regression and classification branch being eliminated by the detections of YOLOv4 and vice versa in the non-maximum suppression step. This method can be used as the object detection algorithm in an image-based people tracking system, namely Tracktor, having a higher inference speed than Faster R-CNN. Our proposed method manages to achieve an overall accuracy of 95% and an inference time of 22 ms.

An advanced deep learning models-based plant disease detection: A review of recent research

“…In recent years, various algorithms for object detection based on DL have been developed. These include single-stage networks such as SSD (W. Liu et al., 2016 ) and YOLO ( Dumitrescu et al., 2022 ; Peng and Wang, 2022 ; Shoaib and Sayed, 2022 ), as well as a networks with multi-stages, like YOLOv1 ( Nasirahmadi et al., 2021 ). These techniques are commonly employed in the identification of plant lesions and pests.…”

A Practical App for Quickly Calculating the Number of People Using Machine Learning and Convolutional Neural Networks

“…Recently, convolutional neural networks (CNNs) have been widely used in image recognition [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Wu et al [7] proposed a multi-task CNN for face detection and head pose estimation by extracting more representative features.…”