International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid evolution of autonomous driving technology has sparked significant interest in developing systems that can operate safely and efficiently in dynamic real-world environments. A critical component of any self-driving car system is its ability to perceive and detect objects in its surroundings. Accurate and real-time object detection ensures that the vehicle can make informed decisions to avoid obstacles, follow traffic rules, and interact with other vehicles and pedestrians safely. This paper explores the implementation of real-time object detection for a self-driving car using the Haar Cascade Classifier, a widely recognized machine learning technique for object detection. The Haar Cascade Classifier is based on the concept of using Haar-like features, which are simple rectangular patterns, to detect objects by analyzing the differences in pixel intensity within those regions. These features, when combined in a cascade of increasingly complex classifiers, allow for the rapid detection of objects such as pedestrians, vehicles, traffic signs, and other critical obstacles. In this implementation, we utilize a pre-trained Haar Cascade model to detect objects from the camera feed of a self-driving car, processing the video in real time. To achieve real-time performance, we optimize the algorithm by carefully adjusting parameters such as the detection scale and window size, as well as reducing false positives through post-processing techniques. The system is tested under different environmental conditions, such as varying lighting, weather, and object sizes, to assess the robustness and effectiveness of the approach in practical scenarios. The results demonstrate that while the Haar Cascade Classifier can achieve reasonable accuracy and speed for detecting simple objects in structured environments, challenges remain in more complex and dynamic situations, such as heavy traffic or poorly lit roads.

Keywords: Real-Time Object Detection, Self-Driving Car, Haar Cascade Classifier, Autonomous Vehicles, Machine Learning, Computer Vision, OpenCV, Image Processing, Traffic Sign Detection, Pedestrian Detection, Vehicle Detection, Artificial Intelligence (AI), Feature Extraction

Downloads: | DOI: 10.17148/IJARCCE.2025.1411135