Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup

• URL: http://arxiv.org/abs/2007.11876v1
• Date: Thu, 23 Jul 2020 09:31:32 GMT
• Title: Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup
• Authors: Gabriel Van Zandycke, Christophe De Vleeschouwer
• Abstract summary: This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. Our inference model can run in real time without the delay induced by a temporal analysis.
• Score: 23.17839603118139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.

Related papers

• Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis [31.43159668073136]
  Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Traditional methods use Graph Neural Networks (GNNs) or Transformers to analyze spatial while RNNs to model temporal dependencies. This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN.
  arXiv  Detail & Related papers  (2024-08-23T14:06:30Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Efficient Single Object Detection on Image Patches with Early Exit Enhanced High-Precision CNNs [0.0]
  This paper proposes a novel approach for detecting objects using mobile robots in the context of the RoboCup Standard Platform League. The challenge lies in detecting a dynamic object in varying lighting conditions and blurred images caused by fast movements. To address this challenge, the paper presents a convolutional neural network architecture designed specifically for computationally constrained robotic platforms.
  arXiv  Detail & Related papers  (2023-09-07T07:23:55Z)
• Transferability of Convolutional Neural Networks in Stationary Learning Tasks [96.00428692404354]
  We introduce a novel framework for efficient training of convolutional neural networks (CNNs) for large-scale spatial problems. We show that a CNN trained on small windows of such signals achieves a nearly performance on much larger windows without retraining. Our results show that the CNN is able to tackle problems with many hundreds of agents after being trained with fewer than ten.
  arXiv  Detail & Related papers  (2023-07-21T13:51:45Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network [0.0]
  Recent state-of-the-art (SOTA) approaches are proposed based on taking a matching network with a heavy structure to distinguish the target from other objects in the area. In this article, a special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot. The presented tracker performs comparatively with the SOTA in challenging situations while having a super speed compared to them (up to $120 FPS$ on 1080ti)
  arXiv  Detail & Related papers  (2021-03-28T11:02:14Z)
• TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking [8.791710193028903]
  This study follows many previous approaches to multi-object tracking (MOT) that model the problem using graph-based data structures. We create a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps. We also provide solutions and propositions for the computational problems that need to be addressed to create a memory-efficient, real-time, online algorithm.
  arXiv  Detail & Related papers  (2021-01-11T21:52:25Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.