Person identification with aerial imaginary using SegNet based semantic segmentation

Abstract

In recent days, people in remote area suffer a lot due to variety of natural calamities such as flooding, earthquake and so on. It has been noted that people used to stay in top portions of their house when there is a flooding issue. Hence, it is very difficult for the rescue team to identify the location of a person by looking at the parts of a person such as hands, legs and partial image of a face using the existing approaches. In this proposed approach, an idea for detecting person when there is only parts such as legs, hands are visible from remote, wild or non-urban areas with the help of UAV-Unmanned Aerial Vehicle has been suggested. Detecting person and identifying the location from the image tends to be a difficult process due to very small and camouflaged objects in the images collected. In this approach, Semantic Segmentation using deep learning approach has been applied in order to detect a person. SegNet- Segmentation Network is the network architecture used in the process of semantically segment the image according to each pixel, hence identifying person is easy. The main objective of this proposed model is that, sometime UAV image may contain partial person images, like legs, hand, etc., that could not be identified by existing approaches were being recognized and identified successfully. This model is trained and tested using HERIDAL dataset. Over 70% images were trained and 30% images were used for testing. This enhanced deep learning model named as Semantic SegNet model achieved an accuracy of 91.04%. This proposed Semantic SegNet model has been compared with existing approaches such as VGG16, GoogleNet and ResNet- Residual neural Network for the same set of trained and tested images. Comparison table declared that this proposed Semantic SegNet Model outperformed other existing models.