Neural network based image object detection and tracking for security and surveillance

Abstract

The saliency map obtained from the source image determines the efficacy of the traditional seam carving process. The importance map proposed in this paper is used to highlight the shadows and important objects in the images. It combines the saliency map, shadow map, and gradient map acquired from the image to discover the image’s prominent regions. The proposed map, when compared to others, highlights more distinct details with the state-of-the-art. The improved seam carving technique is paired with cropping and warping image retargeting operators in the suggested hybrid sequence. By labelling a picture with a class label and object localisation, the coordinates of the objects are generated using R-CNN object detection techniques. This will help in identifying the non-salient objects from the image for security and surveillance purposes with pin-point accuracy.