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Improvement Methods for Assignment Problem in Multi-target Tracking
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
2019 (engelsk)Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
Abstract [en]

Using computer vision to track moving objects is an important but complicated task. In particular, tracking human motion is difficult, since the motion is often complex with motion in three dimensions making it hard to track and model. Accurate position tracking is, however, important in order to estimate the performance during strength and agility training since the position data can be used for several measures. For example, from the position the maximum velocity in a sprint and force development weightlifting can be assessed. In this project, the aim was to implement a software capable of performing multi-target tracking during human motion. We evaluated two specific algorithms optimized for this: the Hungarian algorithm and the Kalman filter, in which the latter was expanded into the interactive multiple model (IMM) algorithm. We implemented these algorithms and verified the performance using a simple multi-target tracking scenario. The results showed that these methods are fast and that they represent a good foundation for displaying measurement results in real-time. Furthermore, they provide a basis for handling more advanced problems related to multitarget tracking, such as object occlusion and collisions. We validated the software output using reference data obtained from a 1080 sprint and 1080 quantum device during a few exercises. In general, the software produces similar output as the reference data on a large scale. On a more local scale, however, the residuals between the camera and 1080 devices vary depending on the exercise. For example, in a squat jump exercise, a typical residual in position is around 5 cm. Whereas, for short-distance sprint, the residuals can be up to 40 cm, although these are partly due to differences in the measuring techniques themselves. The results imply that the algorithms can still be modified to achieve higher accuracy. As the quality of the output seems to vary depending on the tracked motion, it is possible that unique sub-routines may need to be tailored for each exercise. Still, the implementations made during the project can be used in simple measurement scenarios, and serve as a good basis for future work.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-156107OAI: oai:DiVA.org:umu-156107DiVA, id: diva2:1286151
Eksternt samarbeid
Photon Sports Technologies AB
Fag / kurs
Examensarbete i teknisk fysik
Utdanningsprogram
Master of Science Programme in Engineering Physics
Veileder
Examiner
Tilgjengelig fra: 2019-02-12 Laget: 2019-02-06 Sist oppdatert: 2019-02-12bibliografisk kontrollert

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