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Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model's performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a finetuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.