Fast and accurate vision-based pattern detection and identification

Bruce, James; Veloso, Manuela

doi:10.1109/robot.2003.1241768

Cited by 46 publications

(31 citation statements)

References 7 publications

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“…For instance in [7], colour-coded circular markers are arranged in various shapes such as circle, triangle, square, etc. However, such a marker system can have a higher possibility of false detection if the circles outside the region of interest are incorrectly detected.…”

Section: Fiducial Designmentioning

confidence: 99%

Low-latency Vision-based Fiducial Detection and Localization for Object Tracking

Singh¹,

Ha²,

Wood³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Real-time vision systems are widely-used in construction and manufacturing industries. A significant proportion of computational resources of such systems is used in fiducial identification and localisation for motion tracking of moving targets. The requirement is to localise a pattern in an image captured by the vision system precisely, accurately, and with a minimum available computation time. As such, this paper presents a class of patterns and, accordingly, proposes an algorithm to fulfil the requirement. Here, the patterns are designed using circular patches of concentric circles to increase the probability of detection and reduce cases of false detection. In the detection algorithm, the image captured by the vision system is first scaled down for computationally-effective processing. The scaled image is then separated by filtering only the colour components, which are made up of outer circular patches in the proposed pattern. A blob detection algorithm is then implemented for identifying inner circular patches. The inner circles are then localised in the image by using the colour information obtained. Finally, the localised pattern, along with the camera and distortion matrix of the vision system, is applied in a perspective-n-point solving algorithm to estimate the marker orientation and position in the global coordinate system. Our system shows significant enhancement in performance of fiducial detection and identification and achieves the required latency of less than ten milliseconds. Thus, it can be used for infrastructure monitoring in many applications that involve high-speed real-time vision systems.

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Section: Fiducial Designmentioning

confidence: 99%

Low-latency Vision-based Fiducial Detection and Localization for Object Tracking

Singh¹,

Ha²,

Wood³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Perception is provided by two, in our case, overhead cameras, feeding into a central computer to process the image and locate the 10 robots and the ball on the field at a 60Hz rate. We have developed successful algorithms for effective real-time color segmentation and pattern detection [5]. The detected robot locations are fed into an extended Kalman filter for tracking and velocity estimation, and then passed to our soccer strategy and control, which consists of three major components: (1) world state evaluation and play selection; (2) skills and tactics; (3) navigation.…”

Section: Integrated Robot Soccer Architecturementioning

confidence: 99%

CMDragons: Dynamic passing and strategy on a champion robot soccer team

Bruce

Zickler

Licitra

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-After several years of developing multiple RoboCup small-size robot soccer teams, our CMDragons robot team achieved a highly successful level of performance, winning both the 2006 and 2007 competitions without losing a single game. Our small-size team consists of five executing wheeled robots with centralized, off-board perception and decision making. The decision making framework consists of a set of layered components, consisting of perception, evaluation and strategy, robot tactics and skills, and real-time navigation. In this paper, we present the strategy, action selection, and execution aspects of our architecture, with a focus on passing as an example of effective coordinated teamwork. The design enabled our robot team to score using multiple methods, from direct shooting up to 3D passes deflected in midair, resulting in a rich set of actions that were difficult for adversaries to counter. We provide several performance quantified claims supported by testing in our laboratory and in competition settings.

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“…The two basic requirements of a representation are the determination of identity and orientation (since the remaining item of interest, velocity, can be obtained through knowing these over time). Previous research (Bruce and Veloso, 2003) has shown that asymmetrical patterns can be used to allow a range of objects can be identified with fewer colours, and these ideas were extended to develop a representation and associated matching mechanism for tracking objects while minimizing the need for predefined colours.…”

Section: Ergo: Removing Dependence On Predefined Coloursmentioning

confidence: 99%

“…For example, global vision systems for robotic soccer (e.g. (Bruce and Veloso, 2003;Browning et al, 2002;Simon et al, 2001;Ball et al, 2004)) generally require a camera to be mounted perfectly overhead in order to provide a simple geometric perspective (and thus ensure that any object is the same size in the image no matter where in the field of view it appears), simplify tracking, and eliminate complex problems such as occlusion between agents. If a camera cannot be placed perfectly overhead, these systems cannot be used.…”

Section: Introduction: Global Visionmentioning

confidence: 99%