Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network
This work provides a robust solution for object detection and tracking, which is important for applications like autonomous driving and robotics, by demonstrating high accuracy on a specific task.
This paper developed "CueNet," a Fully Convolutional Neural Network for detecting and tracking a cueball in a labyrinth game. CueNet V1 achieved 99.6% accuracy, while CueNet V2, which uses three consecutive input images, improved accuracy to 99.8%.
The main topic of this paper is a brief overview of the field of Artificial Intelligence. The core of this paper is a practical implementation of an algorithm for object detection and tracking. The ability to detect and track fast-moving objects is crucial for various applications of Artificial Intelligence like autonomous driving, ball tracking in sports, robotics or object counting. As part of this paper the Fully Convolutional Neural Network "CueNet" was developed. It detects and tracks the cueball on a labyrinth game robustly and reliably. While CueNet V1 has a single input image, the approach with CueNet V2 was to take three consecutive 240 x 180-pixel images as an input and transform them into a probability heatmap for the cueball's location. The network was tested with a separate video that contained all sorts of distractions to test its robustness. When confronted with our testing data, CueNet V1 predicted the correct cueball location in 99.6% of all frames, while CueNet V2 had 99.8% accuracy.