Pedestrian Detection

Pedestrian detection technology is a critical component of modern intelligent transportation systems, acting as an advanced driver assistance system (ADAS) designed to identify people in a vehicle's path. Intelligent surveillance, sophisticated driver assistance systems, and driverless cars all depend on pedestrian detection. The pedestrian identification system presented in this research is based on deep learning and is intended to operate in complicated urban contexts with high accuracy and real-time performance. The suggested method efficiently detects pedestrians under various illumination conditions, occlusions, and scale variations by using convolutional neural networks with multi-scale feature extraction. Techniques for data augmentation and transfer learning are used to improve the generalization and resilience of the model. When tested on common benchmark datasets, the system shows better detection accuracy and fewer false positives than conventional techniques. The suggested framework offers a dependable and effective solution for practical pedestrian detection applications, according to experimental results.