Survey on Object Detection Framework: Evolution of Algorithms

Object Detection is one among the most emerging and effective field of interest in the broad domain of Artificial Intelligence. Given a set of images or video (sequence of frames) as input, object detection involves detecting and identifying various range of artifacts in the corresponding image or frame. This in turn includes various categories of detecting an artifact like detecting an object based on its location or detecting an object based on its identity. This categorization of the object detection framework is important because of the ranges of application that's available where these techniques can be applied effectively. This paper establishes a detailed survey which explains the different techniques and algorithms that has paved our way into the object detection framework. Starting from the historical mathematical technique dynamic programming which followed geometric techniques to detect objects, techniques like SIFT, HOG which followed feature level detection and networks like D-CNN, R-CNN, RFCN, YOLO etc. which employs a pipeline of network to detect and segment instance of objects from the images.