Improve Detection and Tracking of Pedestrian Subclasses by Pre-Trained Models

Abstract

There are sub-classes of pedestrians that can be defined and it is important to distinguish between them for the detection in autonomous vehicle applications, such as elderly, and children, to reduce the risk of collision. It is necessary to talk about effective pedestrian tracking besides detection so that object remains accurately monitored, here the effective pre-trained algorithms come to achieve this goal in real-time. In this paper, we make a comparison between the detection and tracking algorithms, we applied the transfer learning technique to train the detection model on new sub-classes, after making Images augmentation in previous work, we got better results in detection, reached 0.81 mAP in real-time by using Yolov5 model, with a good tracking performance by the tracking algorithm dependent on detection Deep-SORT.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

“…Fine-tuning significantly improved the performance of pre-trained models, resulting in disease classification accuracy rates exceeding 93%. VGG16, InceptionV3, and Xception achieved accuracy rates surpassing 99%, demonstrating the effectiveness of transfer learning, as previously shown in [27], and the positive impact of fine-tuning on disease detection in maize leaves [26].…”

“…Based on the outcomes of the present comparison, a correlation between the present study's objective and our previous research can be established, enhancing the detection and tracking of pedestrians through an investigation of various versions of the YOLO algorithm and optimizing the StrongSORT algorithm specifically for pedestrian tracking [27]. As a result, more effective methodology for detecting and tracking pedestrians can be developed, surpassing previous findings.…”