A Real-Time Ball Detection Approach Using Convolutional Neural Networks

Teimouri, Meisam; Delavaran, Mohammad Hossein; Rezaei, Mahdi

doi:10.1007/978-3-030-35699-6_25

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…Teimouri et al [14] proposed a new method for detecting soccer ball for low-cost humanoid robot, which achieves high accuracy of up to 97.17% in which the entire ball is extracted by recursive algorithm using key image-based feature after which a light weight CNN is used. Houliston and Chalup [15] proposed a geometric input transformation called visual mesh to generate a plot on visual space, which reduces the complexity in computation, by standardizing the pixel and the object's feature density.…”

Section: The Development Trend In Robocup On Object Detection and Tra...mentioning

confidence: 99%

Object detection on robosoccer environment using convolution neural network

Steffi¹,

Mehta²,

Venkatesh

2022

IJEECS

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Robots with autonomous capabilities depend on vision capabilities to detect and interact with objects and their environment. In the field of robotic research, one of the focus areas is the robosoccer platform that is being used to implement and test new ideas and findings on computer vision and decision making. In this article, an efficient real-time object detection algorithm is employed in a robosoccer simulation environment by deploying a convolution neural network and Kalman filter based tracking algorithms. This study's objective is to classify nao, ball, and the goalpost as well as to validate nao and ball tracking without human intervention from initial frame to last frame. In comparison with the existing methods, the proposed method is robust and fast in identifying three classes namely nao, ball, and goalpost with a speed of 1.67 FPS and a mAP of 95.18%. By implementing this approach, soccer playing robots can make appropriate decisions during game play.

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Section: The Development Trend In Robocup On Object Detection and Tra...mentioning

confidence: 99%

Object detection on robosoccer environment using convolution neural network

Steffi¹,

Mehta²,

Venkatesh

2022

IJEECS

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“…[59] uses a random forest approach for learning more discriminative attributes. Hierarchy and Exclusion (HEX) [31] considers relations between objects and attributes and maps the visual features [130,161] of the images to a set of scores to estimate labels for unseen categories. [8] takes on an unsupervised approach where they capture the relations between the classes and attributes with a three-dimensional tensor while using a DAP-based scoring function to infer the labels.…”

Section: Attribute Classifiersmentioning

confidence: 99%

“…In UDA [71] a non-linear projection from feature space to semantic space (word vector and attribute) is proposed in an unsupervised domain adaptation problem based on regularised sparse coding. [84] uses a deep neural network [161] regression which generates pseudo attributes for each visual category via Wikipedia. LATEM [185] constructs a piece-wise non-linear compatibility function alongside a ranking loss.…”

Section: Label Embeddingmentioning

confidence: 99%

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Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Rezaei

Shahidi

2020

SSRN Journal

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The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection = recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few = one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection = recognition systems using ZSL.

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“…Active Vision, by definition, is the method of actively planning, manipulating, and adjusting the camera viewpoint, with the goal of obtaining the most optimised information from a given environment, which can be either static or dynamic. Object/landmarks detection in an environment is an important research problem that has already been addressed and studied by many researchers such as in (Rezaei and Klette, 2017), (Teimouri et al, 2019). However, in active vision, the goal is to reach the viewpoints which contain most important landmarks and objects so that there is a chance to detect them.…”

Section: Introductionmentioning

confidence: 99%

Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning

Khatibi¹,

Teimouri²,

Rezaei³

2020

Preprint

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In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation while keeping the ball into its viewpoint. Active vision is critical for humanoid decision-maker robots with a limited field of view. To deal with an active vision problem, several probabilistic entropy-based approaches have previously been proposed which are highly dependent on the accuracy of the self-localisation model. However, in this research, we formulate the problem as an episodic reinforcement learning problem and employ a Deep Q-learning method to solve it. The proposed network only requires the raw images of the camera to move the robot's head toward the best viewpoint. The model shows a very competitive rate of 80% success rate in achieving the best viewpoint. We implemented the proposed method on a humanoid robot simulated in Webots simulator. Our evaluations and experimental results show that the proposed method outperforms the entropy-based methods in the RoboCup context, in cases with high self-localisation errors.

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A Real-Time Ball Detection Approach Using Convolutional Neural Networks

Cited by 14 publications

References 16 publications

Object detection on robosoccer environment using convolution neural network

Object detection on robosoccer environment using convolution neural network

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning

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