Armando Sousa

Survey on team tracking techniques applied to sports

Recent years have brought an increasing interest on analyzing efficiently the performance of sports players during training sessions and games. The information collected from such analysis is very valuable to educators and coaches since it allows them to better understand the difficulties of a trainee, a player or even an entire team and formulate adequate training and strategic plans accordingly. In order to perform this analysis in a consistent and systematic way, sophisticated sensory systems and data processing techniques are needed. This paper presents a survey on relevant work, current techniques and trends on the area of team tracking systems applied to sports. We propose a classification of these systems by distinguishing them into two main categories: intrusive and nonintrusive. Nonintrusive systems are further refined into outdoor and indoor sports applications. The specific characteristics of each system are itemized, including the identification of the strong points and limitations. Finally, the paper highlights some open issues and research opportunities on this area.

Real Time Colour Based Player Tracking in Indoor Sports

In recent years there has been a growing interest by the sport’s experts (teachers and coaches) in developing automatic systems for detecting, tracking and identifying player’s movements with the purpose of improving the players’ performance and accomplishing a consistent and standard analysis of the game metrics. A challenge like this requires sophisticated techniques from the areas of image processing and artificial intelligence. The objective of our work is to study and develop hardware and software techniques in order to build an automatic visual system for detecting and tracking players in indoor sports games that can aid coaches to analyse and improve the players’ performance. Our methodology is based on colour features and therefore several colour image processing techniques such as background subtraction, blob colour definition (RGB and HSL colour spaces) and colour blob manipulation are employed in order to detect the players. Past information and players’ velocity allow the tracking algorithm to define probable areas. Tests conducted with a single IP surveillance camera on the sports hall of the Faculty of Sports from the University of Porto showed detection rates from 72.2% to 93.3%.

Tracking and Identifying in Real Time the Robots of a F-180 Team

This paper describes the method employed to track and identify each robot during a Robocup match. Also, the playing ball is tracked with almost no extra processing effort. To track the robots it is necessary the use of adequate markers so that not only the position is extracted but also the heading. We discuss the difficulties associated with this problem, various possible approaches and justify our solution. The identification is performed thanks to a minimalist bar code placed in each robot. The bar code solves the problem of resolving some ambiguities that can arise in certain configurations. The procedure described can be executed in real time as it was shown in Paris in RoboCup-98.

Vision system for tracking handball players using fuzzy color processing

The sports community needs technological aid to extract accurate statistics and performance data from both practice sessions and games. To obtain such information, players must be tracked over time and their movements processed so that individual actions and team plays are simultaneously analyzed. In order to perform this analysis in an automated, formal and accurate way, the authors developed a cost conscientious processing system fed by two overhead cameras (roughly one video stream for each half-field). Players are detected by vest colors, and Fuzzy Logic is used to allow for a given color to be shared by different teams. Color models for the background and the teams are dynamic over time to make up for changes in natural lighting conditions and consequent color changes. Player tracking is further enhanced using Kalman Filtering. Some examples of the analysis, made possible by the proposed system, are shown. Results are based on videos collected during the Portuguese Handball SuperCup competition for the year 2011.

5dpo-2000 Team Description

This paper describes the 5dpo-2000 team. The paper will be divided into three main sections, corresponding to three main blocks: the Global Level, the Local Level and the Interface Level. These Levels, their subsystems and some implementation details will be described next.

Optimization Approach for the Development of Humanoid Robots’ Behaviors

Humanoid robots usually have a large number of degrees of freedom which turns humanoid control into a very complex problem. Humanoids are used in several RoboCup soccer leagues. In this work, the SimSpark simulator of the Simulation 3D will be used. This paper presents an automatic approach for developing humanoid behaviors based on the development of a generic optimization system. The paper describes the adopted architecture, main design choices and the results achieved with the optimization of a side kick and a forward kick. For both skills, the optimization approach allowed the creation of faster and more powerful and stable behaviors.

Self localization of an autonomous robot: using an EKF to merge odometry and vision based landmarks

Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a extended Kalman filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown

Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms

Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1?-3??degrees in rotation error while requiring only 5-35?ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise. 3/6 DoF localization system capable to operate in cluttered/dynamic and challenging environments.Efficient C++ ROS implementation with multi-level point cloud registration and recovery.Robust initial pose estimation using geometric feature matching.2D/3D mapping with integration of full sensor data or only unknown areas.Fully configurable and modular processing pipeline, extensible to other tasks besides self-localization.

Agricultural Wireless Sensor Mapping for Robot Localization

Crop monitoring and harvesting by ground robots in steep slope vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the Global Positioning System (GPS). In this paper the use of agricultural wireless sensors as artificial landmarks for robot localization is explored. The Received Signal Strength Indication (RSSI), of Bluetooth (BT) based sensors/technology, has been characterized for distance estimation. Based on this characterization, a mapping procedure based on Histogram Mapping concept was evaluated. The results allow us to conclude that agricultural wireless sensors can be used to support the robot localization procedures in critical moments (GPS blockage) and to create redundant localization information.

Flexible pick and place architecture using ROS framework

The need for efficient automation methods has prompted the rapid development in the field of Robotics. The development of intelligent robots leads to the ability of them becoming an operator highly efficient and able to adapt to a wide range of problems. Still, despite of the several robotic solutions available, the majority of current industrial robots do not use the Robotic Operative System (ROS) and have limitations in terms of autonomously correct errors during their tasks. An important aspect to consider when developing a new robot is the selection of the methodology for recognition of the objects to be handled. In this paper, it will be presented an approach with enough flexibility to be potentially applicable to different scenarios of object recognition and handling normally found in industrial environment. The main aim is for this application to be applied to pick and place routines in robotics. Moreover it will be presented one particular case study that used the proposed approach, the European Robotics Challenges (EUROC) - a challenge aiming to develop a robot for shop floor logistics and manipulation. Our proposed approach is based on the three tiers paradigm: 1-recognition/sensing tier, 2-effector tier and 3-the control tier and was built using the ROS framework. Since ROS is becoming one of the most useful tools in robotics nowadays, the possibility of using a methodology able to be expressed in ROS allows for the development of a standard approach to pick and place operations. Another advantage of our proposed pick and place approach is the ability to have a robot safely and efficiently inserted in an unknown environment. This is possible due to the insertion of an adaptive control tier in our methodology. The proposed approach can be valuable in the field of robotics and can be potentially applied in multiple tasks and it has already allowed us to advance to the next stage of the already mention challenge.