Vehicle Impact Detection System

Computer Science And Engineering

Object detection is widely used to extract data from images for further computation. It is a technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as animals, tables, or cars) in digital images and videos and has applications in many areas of computer vision, including image retrieval, part identification, part inspection and video surveillance. An image classification or image recognition model simply detect the probability of an object in an image and this of pivotal importance in it’s detection. In contrast to this, object localization refers to identifying the location of an object in the image. An object localization algorithm will output the coordinates of the location of an object with respect to the image. In computer vision, the most popular way to localize an object in an image is to represent its location with the help of bounding boxes. By applying object detection, you’ll not only be able to determine what is in an image, but also where a given object resides. Constructing an object detection dataset will cost more time, yet it will result most likely in a better model. The above method can be used for image processing, background subtraction or for video processing and aids in it’s assistance. This paper puts forth the idea of using said processed images for detecting vehicles in front of the camera in order to detect the possibility of a collision and warn the user accordingly using techniques like Histogram of Oriented Gradients (HoG), Machine Learning algorithms such as SVM, MLP, XGB and distance measure (Chi - squared).

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