Survey On Real Time Multiple Object Detection Using MobileNet-SSD with Opencv

Abstract

The field of computer vision known as real-time object detection is vast, dynamic, and difficult. Real-time object recognition and tracking are critical and difficult problems in many computer vision applications, including video surveillance, robot navigation, and vehicle navigation. Detecting an item in a video sequence is what object detection is all about. Every tracking technique necessitates an object detection mechanism, either in each frame or whenever an item appears for the first time in the video sequence. The practise of identifying one object or numerous objects using a static or dynamic camera is known as object tracking. The availability of powerful computers, high-quality, low-cost video cameras will enhance the need for automated video analysis. Image Localization is used when there is only one object to distinguish in an image, and Object Detection is used when there are multiple objects in an image. The most often used strategies for contemporary deep learning models to perform various tasks on embedded devices are mobile networks and binary neural networks. In this research, we propose a method for distinguishing an item based on the MobileNet deep learning pre- prepared model for Single Shot Multi-Box Detector (SSD). This technique is used to recognise objects in a video stream in real time and for webcam broadcasting. Following that, we use an object detection module to determine what is in the video stream. To complete the module, we combine the MobileNet and SSD frameworks to create a fast and efficient deep learning-based item detection technique.