PEDESTRIAN ALERT SYSTEM USING DEEP LEARNING

Abstract

Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers (e.g., Volvo, Ford, GM, Nissan) offer this as an ADAS option in 2017. It is used in many cars and vehicles for automatic driver assistance systems. But when the ADAS system detects pedestrians but if fails to apply brakes on time accidents might happened. To overcome this situation our project provides an audio alert to the human passengers or driver in the vehicle so that they can apply manual brakes. In this project we will require Python libraries such as Pytorch and OpenCV. Pytorch is a widely used Machine Learning library. It is popular for the YOLO (You Only Look Once) algorithm which is built for Object Detection, we are using the YOLO algorithm and customize it to detect objects on a pedestrian dataset. Python has a library pyttsx3, that is capable to convert text-to-speech offline. Itextracts the label from the detected pedestrians in the video and converts the text label into speech. The algorithm detects the pedestrians when they are in a close approach to the vehicles and sends the identifies labels to the pyttsx3 speaker engine. An audio alert is generated by the engine and alerts the passengers or drivers. Keywords - YOLO, Pytorch, Pyttsx3, Object Detection, Python.