Object Detection and Classification for Self-Driving Cars

With the advancement in image processing, object detection has been one of the interesting topics due to its spectrum of applications in real time. For past 10 years Advanced Driving Assistance System (ADAS) has rapidly grown. Recently not only luxury cars but some entry level cars are equipped with ADAS applications, such as Automated Emergency Braking System (AEBS).ADAS systems are used for assisting the drivers by providing advice and warnings when necessary.Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems.This application is for multiple object detection and classification in a given video based on Open Computer Vision (OpenCV) libraries. The application also uses MobileNet architecture, SSD (Single Shot Detectors) framework and Caffe (Convolutional Architecture for Fast Feature Embedding) model to get the predictions.The system is used as one of the features in ADAS system for collision avoidance by detecting and classifying the objects such as vehicles and pedestrian.