Playing Soccer without Colors in the SPL: A Convolutional Neural Network Approach

Muñoz‐Leiva, Francisco; Cruz, Nicolás C.; Bugueño, Ignacio; Ruíz-del-Solar, Javier

doi:10.48550/arxiv.1811.12493

Cited by 1 publication

(1 citation statement)

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“…In [23] authors use CNN to perform localization of soccer ball by predicting the x and y coordinates. In a recent work [17] use proposal generators to estimate regions of soccer ball and further use CNN for the classification of regions. In [13] authors compared various CNN architectures namely LeNet, SqueezeNet, and GoogleLeNet for the task of a ball detection by humanoid robots.…”

Section: Related Workmentioning

confidence: 99%

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

Kukleva

Khan

Farazi

et al. 2019

RoboCup 2019: Robot World Cup XXIII

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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.

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Section: Related Workmentioning

confidence: 99%