Local Normalization and Delayed Decision Making in Speaker Detection and Tracking

2000 ◽  
Vol 10 (1-3) ◽  
pp. 113-132 ◽  
Author(s):  
Johan Koolwaaij ◽  
Lou Boves
Keyword(s):  
Decision Making ◽  
Detection And Tracking ◽  
Speaker Detection

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

scholarly journals Ball and Player Detection & Tracking in Soccer Videos Using Improved YOLOV3 Model

2021 ◽  
Author(s):  
Banoth Thulasya Naik ◽  
Mohammad Farukh Hashmi
Keyword(s):  
Decision Making ◽  
Kalman Filtering ◽  
Video Recording ◽  
The Past ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Sort Algorithm ◽  
Ball Detection ◽  
Player Detection ◽  
Over Time

Abstract Over the past few years, there has been a tremendous increase in the interest and enthusiasm for sports among people. This has led to an increase in the importance given to video recording of various sports that capture even the minutest detail using high-end equipment. Recording and analysis have thereby become extremely crucial in sports like soccer that involve several complex and fast events. Ball detection and tracking along with player analysis have emerged as an area of interest among a lot of analysts and researchers. This is because it helps coaches in performance assessment of the team and in decision making to obtain optimized results. Video analysis can additionally be used by coaches and recruiters to look for new, talented players based on their previously played games. Ball detection also plays a pivotal role in assisting the referees in making decisions at game-changing moments. However, as the ball is almost always moving, its shape-appearance keeps changing over time and it is frequently occluded by players, it makes it difficult to track it throughout the game. We propose a deep learning-based YOLOv3 model for the ball and player detection in broadcast soccer videos. Initially, the videos are processed and unnecessary parts like zoom-ins, replays, etc., are removed to obtain only the relevant frames from each game. Tracking is achieved using the SORT algorithm which employs a Kalman filtering and bounding box overlap.

Human Factors in the Development of Trend Detection and Tracking Techniques

2006 ◽  
pp. 273-279
Author(s):  
Chaomei Chen ◽  
Kaushal Toprani ◽  
Natasha Lobo
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Information Science ◽  
Trend Detection ◽  
Text Document ◽  
Working Environments ◽  
Detection And Tracking ◽  
Emerging Trends ◽  
Efficiency And Effectiveness

Trend detection has been studied by researchers in many fields, such as statistics, economy, finance, information science, and computer science (Basseville & Nikiforov, 1993; Chen, 2004; Del Negro, 2001). Trend detection studies can be divided into two broad categories. At technical levels, the focus is on detecting and tracking emerging trends based on dedicated algorithms; at decision making and management levels, the focus is on the process in which algorithmically identified temporal patterns can be translated into elements of a decision making process. Much of the work is concentrated in the first category, primarily focusing on the efficiency and effectiveness from an algorithmic perspective. In contrast, relatively fewer studies in the literature have addressed the role of human perceptual and cognitive system in interpreting and utilizing algorithmically detected trends and changes in their own working environments. In particular, human factors have not been adequately taken into account; trend detection and tracking, especially in text document processing and more recent emerging application areas, has not been studied as integral part of decision-making and related activities. However, rapidly growing technology, and research in the field of human-computer interaction has opened vast and, certainly, thought-provoking possibilities for incorporating usability and heuristic design into the areas of trend detection and tracking.

Sign in / Sign up

Export Citation Format

Share Document