A Tracker for Multiple Dynamic Targets Using Multiple Sensors

Feldman, Adam S.; Adams, S.; Hybinette, Maria; Balch, Tucker

doi:10.1109/robot.2007.363956

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…The average track length of the basketball players' tracks is 39.81 s, whereas tracks for the slower–moving social behavior experiment participants are an order of magnitude longer, at an average of 339.57 s. These results compare favorably with earlier versions of this tracker, which never surpassed an average track length of 10 s (Feldman, Adams, Hybinette, & Balch, 2007).…”

Section: Resultsmentioning

confidence: 58%

The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets

Feldman

Hybinette

Balch

2012

Journal of Field Robotics

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We describe and evaluate a greedy detection‐based algorithm for tracking a variable number of dynamic targets online. The algorithm leverages the well‐known iterative closest point (ICP) algorithm for aligning target models with target detections. The approach differs from trackers that seek globally optimal solutions because it treats the problem as a set of individual tracking problems. The method works for multiple targets by sequentially matching models to detections, and then removing detections from further consideration once models have been matched to them. This allows targets to pass close to one another with reduced risks of tracking failure due to “hijacking,'' or track merging. There has been significant previous work in this area, but we believe our approach addresses a number of tracking problems simultaneously that have only been addressed separately before. The algorithm is evaluated using four to eight laser range finders in three settings: quantitatively for a basketball game with 10 people and a 25‐person social behavior experiment, and qualitatively for a full‐scale soccer game. We also provide qualitative results using video to track ants in a captive habitat. During all the experiments, agents enter and leave the scene, so the number of targets to track varies with time. With eight laser range finders running, the system can locate and track targets at sensor frame rate 37.5 Hz on commodity computing hardware. Our evaluation shows that the tracking system correctly detects each track over 98% of the time. © 2012 Wiley Periodicals, Inc.

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Section: Resultsmentioning

confidence: 58%

The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets

Feldman

Hybinette

Balch

2012

Journal of Field Robotics

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“…We are exploring other techniques for improving vision based tracking such as region based mechanisms like [Andrade et al (2005)] and using hybrid sensors in poor lighting conditions such as lasers [Feldman et al (2006)] for robustness and precision.…”

Section: ])mentioning

confidence: 99%

Augmenting Live Broadcast Sports with 3D Tracking Information

Cavallaro¹,

Hybinette

White³

et al. 2011

IEEE Multimedia

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In this article we describe a family of technologies designed to enhance live television sports broadcasts. Our algorithms provide a means for tracking the game object (the ball in baseball, the puck in hockey); instrumenting broadcast cameras and creating informative graphical visualizations regarding the object's trajectory. The graphics are embedded in the live image in real time in a photo realistic manner. The demands of live network broadcasting require that many of our effects operate on live video streams at frame-rate (60 Hz) with latencies of less than a second. Some effects can be composed off-line, but must be Preprint submitted to IEEE MultiMedia Magazine complete in a matter of seconds. The final image sequence must also be compelling, appealing and informative to the critical human eye. Components of our approach include:(1) Instrumention and calibration of broadcast cameras so that the geometry of their field of view is known at all times, (2) Tracking items of interest such as balls and pucks and in some cases participants (e.g., cars in auto racing), (3) Creation and transformation of graphical enhancements such as illustrative object highlights (e.g., a glowing puck trail) or advertisements to the same field of view as the broadcast cameras and (4) Techniques for combining these elements into informative on-air images. We describe each of these technologies and illustrate their use in network broadcast of hockey, baseball, football and auto racing. We focus on vision based tracking of game objects.

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“…Range sensors, such as sonar sensors [24,31,47] and laser sensors [24,25,27,28,29,33] are widely used sensors for the robot applications because of their ability to give the location information of the objects in the field of view of the sensor. The laser range finder has the higher accuracy and higher speed, which makes it a reliable tool to detect the object.…”

Section: Sensor Typementioning

confidence: 99%

“…Detecting objects with a laser range finder can be accomplished using two major methods. One is to find the difference between two laser frames [33,48]. This method is feasible when the object is moving.…”

Section: Sensor Typementioning

confidence: 99%

A human tracking mobile robot system based on vision and laser sensor fusion

Dai¹

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A Tracker for Multiple Dynamic Targets Using Multiple Sensors

Cited by 3 publications

References 6 publications

The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets

The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets

Augmenting Live Broadcast Sports with 3D Tracking Information

A human tracking mobile robot system based on vision and laser sensor fusion

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