Robust Deep Transfer Learning Based Object Detection and Tracking Approach

Abstract

At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker, which involves region proposal network (RPN) and Fast R-CNN. The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects. The RPN is a trained model used for the generation of the high-quality region proposals, which are utilized by Fast R-CNN for detection process. Besides, Residual Network (ResNet 101) model is used as a shared convolutional neural network (CNN) for the generation of feature maps. The performance of the ResNet 101 model is further improved by the use of Adam optimizer, which tunes the hyperparameters namely learning rate, batch size, momentum, and weight decay. Finally, softmax layer is applied to classify the images. The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place. The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.