Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking

TL;DR

This paper introduces a CNN-based method that leverages temporal information from image sequences to improve soccer ball detection and tracking, especially under occlusion or disappearance scenarios.

Contribution

It proposes a novel approach combining spatial and temporal convolutional models to enhance detection accuracy and robustness in video sequences.

Findings

Temporal models outperform single-frame detection methods.

Using historical frames improves tracking during occlusion.

The approach achieves high precision and recall in challenging conditions.

Abstract

Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CNN) approach to detect the soccer ball in an image sequence. In contrast to the existing methods where only the current frame or an image is used for the detection, we make use of the history of frames. Using history allows to efficiently track the ball in situations where the ball disappears or gets partially occluded in some of the frames. Our approach exploits spatio-temporal correlation and detects the ball based on the trajectory of its movements. We present our results with three convolutional methods, namely temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve the detection task for an image using fully convolutional encoder-decoder architecture, and later, we use it as an input to our temporal models and jointly learn the detection task in sequences of images. We evaluate all our experiments on a novel dataset prepared as a part of this work. Furthermore, we present empirical results to support the effectiveness of using the history of the ball in challenging scenarios.