A Survey on Human Detection using Reinforcement Learning

Abstract

Human Detection - technology related to computer vision and image processing work by finding people in digital photos and videos and surveillance videos that are part of the observation. Single Shot Detector (SSD) is a deep learning method and is one of the fastest algorithms that use a single convolutional neural network to detect objects involving humans, cats, dogs, etc., and extract feature maps to classify the candidate object in the respective images. The advantage that SSD has is that it is quick to detect and has high accuracy in a given situation compared to regional suggested networks with smaller resolution images and smaller objects. However, it is still somewhat lagging in detecting large objects in larger images as compared to other algorithms that have been used to achieve better accuracy. It is a simple, end-to-end solution for a single network, and detection and extraction are done with one step forward single pass. The proposed system is to use the Optimized-SSD algorithm to detect human accuracy in the proposed database with good accuracy which will be the task of learning to increase SSD capacity as a detection system.