Optimized Detection, Classification, and Tracking with YOLOV5, HSV Color Thresholding, and KCF Tracking

This paper shows a detection and tracking approach regarding position estimation of pots and angle of arrow on-ground. The pots are detected by HSV color thresholding, and then classified on the basis of local positional parameters like distance of pots from the robot and relative position of pots with the robot. This is a computationally efficient solution for simple regular objects like pots. The Kalman filter specifically works on providing better depth estimates and thereby the position of pots even when the pot tables are overlapping. The approach of the detection-tracking algorithm for small objects like arrows is to combine the accuracy of object detection provided by the custom-trained YOLOv5 model and the speed of the KCF tracker to outperform the results. Multithreading is used to concurrently detect and track the arrows in consecutive frames, producing a computationally efficient approach as compared to standalone detection with YOLOv5. This paper also describes an approach to effectively get depth information on an object using an Intel RealSense D435i depth camera.