Video Object Detection with an Improved Classification Approach

Abstract

Object detection is primary task in computer vision. The various CNN are majorly used by researchers to improve the classification and detection of objects present in video frames. Object detection is a prime task in self-driven cars, satellite images, robotics, etc. The proposed work is focused on improvement of object classification and detection in videos for video analytics. The key focus of work is identification and tuning of hyper-parameters in deep learning models. The deep learning-based object detection models are broadly classified into two categories, i.e., one-stage detector and two-stage detector. We have selected one-stage detector for experimentation. In this paper, a custom CNN model is given with hyper-parameter tuning and the results are compared with state of art models. It is found out that the hyper-parameter tuning on CNN models helps in improvement of object classification and detection accuracy of deep learning models.