Object Tracking using CSRT Tracker and RCNN

Abstract

Nowadays, Object tracking is one of the trendy and under investigation topic of Computer Vision that challenges with several issues that should be considered while creating tracking systems, such as, visual appearance, occlusions, camera motion, and so on. In several tracking algorithms Convolutional Neural Network (CNN) has been applied to take advantage of its powerfulness in feature extraction that convolutional layers can characterize the object from different perspectives and treat tracking process from misclassification. To overcome these problems, we integrated the Region based CNN (Faster RCNN) pre-trained object detection model that the OpenCV based CSRT (Channel and Spatial Reliability Tracking) tracker has a high chance to identifying objects features, classes and locations as well. Basically, CSRT tracker is C++ implementation of the CSR-DCF (Channel and Spatial Reliability of Discriminative Correlation Filter) tracking algorithm in OpenCV library. Experimental results demonstrated that CSRT tracker presents better tracking outcomes with integration of object detection model, rather than using tracking algorithm or filter itself.