An enhanced moving target detection and tracking from thermal images using segmentation based DCNN.

The advancement made in image processing techniques has gained much attraction among the researchers in recent years. Object detection and classification are the recent topics that annoys the researchers for developing new techniques in this field. Conventional object detection models degrade the performance due to complexity in feature selection process. Thus, the rapid development in deep learning is combined with object detectors to resolve the addressed problems. In present research, we introduce detecting of an object and classification model using improved segmentation and classifier. The aim of the study is to detect the weapon in human object and then classified onto its relevant classes for upcoming testing models. The proposed model comprises of five phases where each phase serves as input to its predecessor output. Initially, the thermal images are collected from a public repository and then preprocessed using median filtering which eliminates the irrelevant noises by replacing median values to its neighboring pixels. Further, Adaptive Histogram Equalization (AHE) is used for improving the finest quality of an image. The preprocessed image is further fed into segmentation model. Here, OTSUs, one of the successful models is employed for deriving edges preserved segmented image. By computing SURF, GLCM and matching features on segmented images, the relevant features are extracted. These extracted features are then given to DCNNs which develops a layering stack and creates training and testing model. Based on satisfied requirements of training features, testing images are classified to its relevant classes. The performance assessment of classification accuracy dictates 84% of efficiency than the prior works which is implemented using Fuzzy Segmentation. [ABSTRACT FROM AUTHOR]