scholarly journals Active Player Detection in Handball Scenes Based on Activity Measures

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1475 ◽  
Author(s):  
Miran Pobar ◽  
Marina Ivasic-Kos
Keyword(s):  
Detection Method ◽  
Team Sports ◽  
Interest Points ◽  
Visual Appearance ◽  
Additional Information ◽  
Active Player ◽  
Assignment Algorithm ◽  
In The Wild ◽  
Activity Measures ◽  
Player Detection

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.

Perceptual Rules for Building Enhancements in 3D Virtual Worlds

i-com ◽  
2017 ◽  
Vol 16 (3) ◽  
pp. 205-213 ◽  
Author(s):  
Patrick Tutzauer ◽  
Susanne Becker ◽  
Norbert Haala
Keyword(s):  
Virtual Worlds ◽  
Mobile Apps ◽  
Research Question ◽  
Feature Space ◽  
Information Modeling ◽  
Visual Appearance ◽  
Urban Scenes ◽  
Additional Information ◽  
Building Models ◽  
Building Information

AbstractWhile the generation of geometric 3D virtual models has become feasible to a great extent, the enrichment of the resulting urban building models with semantics remains an open research question in the field of geoinformation and geovisualisation. This additional information is not only valuable for applications like Building Information Modeling (BIM) but also offers possibilities to enhance the visual insight for humans when interacting with that kind of data. Depending on the application, presenting users the highest level of detail of building models is often neither the most informative nor feasible way. For example when using mobile apps, resources and display sizes are quite limited. A concrete use case is the imparting of building use types in urban scenes to users. Within our preliminary work, user studies helped to identify important features for the human ability to associate a building with its correct usage type. In this work we now embed this knowledge into building category-specific grammars to automatically modify the geometry of a building to align its visual appearance to its underlying use type. If the building category for a model is not known beforehand, we investigate its feature space and try to derive its use type from there. Within the context of this work, we developed a Virtual Reality (VR) framework that gives the user the possibility to switch between different building representation types while moving in the VR world, thus enabling us in the future to evaluate the potential and effect of the grammar-enhanced building model in an immersive environment.

A Two-Stage Detection Method for Moving Targets in the Wild Based on Microphone Array

2015 ◽  
Vol 15 (10) ◽  
pp. 5795-5803 ◽  
Author(s):  
Feng Guo ◽  
Jingchang Huang ◽  
Xin Zhang ◽  
Yongbo Cheng ◽  
Huawei Liu ◽  
...  
Keyword(s):  
Detection Method ◽  
Microphone Array ◽  
Moving Targets ◽  
Two Stage ◽  
In The Wild

An Efficient Near-Duplicate Video Shot Detection Method Using Shot-Based Interest Points

2009 ◽  
Vol 11 (5) ◽  
pp. 879-891 ◽  
Author(s):  
Xiangmin Zhou ◽  
Xiaofang Zhou ◽  
Lei Chen ◽  
A. Bouguettaya ◽  
Nong Xiao ◽  
...  
Keyword(s):  
Detection Method ◽  
Interest Points ◽  
Shot Detection ◽  
Video Shot ◽  
Duplicate Video

scholarly journals Transition Metal Precipitates in Mc Si: A New Detection Method Using 3D-FIB

2013 ◽  
Vol 205-206 ◽  
pp. 136-141 ◽  
Author(s):  
Annika Zuschlag ◽  
Michail Schwab ◽  
Dorit Merhof ◽  
Giso Hahn
Keyword(s):  
Transition Metal ◽  
Focused Ion Beam ◽  
Detection Method ◽  
Ion Beam ◽  
Minority Charge Carrier ◽  
X Ray ◽  
3 Dimensional ◽  
Additional Information ◽  
Transmission Electron ◽  
Charge Carrier Lifetime

To investigate transition metal precipitates in Si, synchrotron based measurements, like micro x-ray fluorescence (μXRF) or detailed transmission electron microscopy (TEM) studies, are usually necessary. Transition metals are among the most detrimental defects in multi-crystalline (mc) silicon material for solar cell applications, due to their impact on minority charge carrier lifetime and possible shunt formation. We present another possibility to investigate transition metal precipitates by 3-dimensional focused ion beam (3D-FIB) cutting using a combined scanning electron microscope (SEM) SEM-FIB-system. This method is able to detect transition metal precipitates down to 5 nm in radius and provides additional information about the 3D shape, size and spatial distribution of the precipitates.

scholarly journals Optimized Algorithm for Target Tracking using Classification Information based Association Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 932-938
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Traffic Monitoring ◽  
Object Localization ◽  
Target Classification ◽  
Target Class ◽  
Additional Information ◽  
Assignment Algorithm ◽  
Air Surveillance ◽  
Classification Information

In last few decades, multiple target tracking fetches quite attention to the researchers for object localization and monitoring target trajectories which has become one of the most used technique in the area of visual tracking, traffic monitoring, air surveillance system, robotics and vision. On the basis of S-D assignment algorithm, a new algorithm for tracking multiple targets in presence of clutter is designed. By considering target classification information received as special feature from target scan report, cost coefficients of dynamic assignment matrix are modified accordingly using joint probabilistic data association filter. The tracking results get improved with the use of target class and kinematic features information where the association costs are similar for different targets. With the help of the information collected in current scan the classifier output is dynamically updated to incorporate new target classes to be used future scans. Simulation results show that new algorithm can attain competitive tracking performance with distributed computational load by utilizing target classification information into dynamic multidimensional assignment algorithm. The main contribution of this paper is the development of new target tracking method based on IMM filter which generate dynamic classifier to incorporate target features information. This additional information about targets present in current scan helps to take future scans data association decisions.

Fights Behavior Detection Based on Space-Time Interest Points

2014 ◽  
Vol 577 ◽  
pp. 659-663
Author(s):  
Jing Hu ◽  
Xiang Qi ◽  
Jian Feng Chen
Keyword(s):  
Action Recognition ◽  
Visual Analysis ◽  
Detection Method ◽  
Human Action Recognition ◽  
Human Action ◽  
Space Time ◽  
Interest Points ◽  
Behavior Detection ◽  
Motion Energy ◽  
Temporal Features

Human action recognition belongs to the senior visual analysis of computer vision, which involves image processing, artificial intelligence, pattern recognition and so on, is becoming one of the most hot research topic in recent years. In this paper, on the basis of comparative analysis and study towards current methods related to human action recognition, we propose a novel fights behavior detection method which is based on spatial-temporal interest point. Since most information of human action in video are indicated by the space-time interest points of video, we combine spatial-temporal features with motion energy image to describe information of video, and local spatial-temporal features are applied to extract fights behavior model by bags of words. Experimental results show that this method can achieve high accuracy and certain practical value.

Method for Detecting Chinese Texts in Natural Scenes Based on Improved Faster R-CNN

2019 ◽  
Vol 34 (02) ◽  
pp. 2053002
Author(s):  
Shuhua Liu ◽  
Hua Ban ◽  
Yu Song ◽  
Mengyu Zhang ◽  
Fengqin Yang
Keyword(s):  
Chinese Text ◽  
Detection Method ◽  
Lower Layer ◽  
Image Features ◽  
Text Detection ◽  
Natural Scenes ◽  
Scene Text Detection ◽  
Text Feature ◽  
In The Wild ◽  
Chinese Texts

In this study, a natural scene text detection method based on the improved faster region-based convolutional neural network (R-CNN) is proposed. This method extracts image features with the Inception-ResNet architecture, adopts a region proposal network to generate region proposals for the extracted features, merges the fine-tuned features with the region proposals, and finally, uses Fast R-CNN to classify and locate text. The proposed method solves the problems of varying text sizes and the text being obscured in the image. Compared with the original Faster R-CNN, the multilevel Inception-ResNet network model presented in this study can extract deeper text features. The extracted feature map is further sparsely represented by Reduction B, Inception ResNet C and Avg Pool, and then is fused with text regions obtained by the text feature mapping lower layer network to acquire the exact text regions. The text detection method presented in this study is tested on the 2017 dataset of ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17), which contains a large number of distorted, blurry, different scale and size texts. An accuracy of 76.4% is achieved in this platform, thereby proving the efficiency of the proposed method.

scholarly journals A Fully Automatic Player Detection Method Based on One-Class SVM

2013 ◽  
Vol E96.D (2) ◽  
pp. 387-391 ◽  
Author(s):  
Xuefeng BAI ◽  
Tiejun ZHANG ◽  
Chuanjun WANG ◽  
Ahmed A. ABD EL-LATIF ◽  
Xiamu NIU
Keyword(s):  
Detection Method ◽  
Fully Automatic ◽  
Player Detection
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