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Video Tracking Algorithm for UnmannedAerial Vehicle Surveillance
KTH, School of Electrical Engineering (EES), Signal Processing.
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

On behalf of Avia Satcom Co., Ltd. we have conducted research in the eld of Computer Vision. Avia Satcom Co., Ltd. is in the stage of developing an Unmanned Aerial Vehicle and is interested in developing an algorithm for video tracking of arbitrary ground objects. The key requirements imposed on the tracker are: (1) being able to track arbitrary targets, (2) track accurately through challenging conditions and (3) performing the tracking in real-time. With these requirements in mind, we have researched what has been done in the eld and proposed an algorithm for video tracking. Video tracking in general is the process of estimating, over time, the location of one or multiple objects in a video captured by an analogue camera. Video Tracking is often divided into Target Representation, Target Detection and Target Tracking. First you create a model of the target. Then you implement techniques for detecting the object using the target model. Lastly, you use dierent techniques to predict the new target location given the current location.

After comparing some trackers we chose to implement the Particle Filter. The Particle Filter is a robust tracker which can handle partial occlusion and non-Gaussian distributions. To represent the target we used Haar-like rectangular features and Edge Orientation Histograms features. The features have been shown to be robust and simple to calculate. Moreover, in a combination they have shown to serve well as each other complements, addressing each others shortcomings.

We tested our tracker on a set of videos with challenging scenarios. Although the tracker could handle some scenarios if the parameters were tuned correctly, it did not perform satisfactory when dealing with appearance changes.

To address the issue we extended our approach and implemented a strong classifier to help aid our observation model. The strong classifier consists of several trained weak classi ers. The strong classifier can classify estimates as either foreground or background and output a value of how con dent it is.

Along with the classifier we also de ned a completely new set of features. With the new approach our tracker is significantly more robust and can handle all scenarios well. However, due to some minor error in our adaptive procedure we were not able to learn appearance changes over time, only the initial appearance. Therefore, any larger appearance changes still remain a challenge for the tracker. We do believe this is just a coding error which can be easily fixed. Nevertheless, the tracker still performs satisfactory in tracking arbitrary objects. We did however find that the tracker had difficulties with small objects and was not as robust in maintaining track lock in that case.

For future work we would like to investigate the error preventing us from adapting our weak classi ers over time. We would also like to look into a technique for locating extremely small moving objects using Background Subtraction which could act as a good complement to what has already been done.

Place, publisher, year, edition, pages
2012. , 76 p.
EES Examensarbete / Master Thesis, XR-EE-SB 2012:010
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-98206OAI: diva2:535572
Subject / course
Signal Processing
Educational program
Master of Science in Engineering - Information and Communication Technology
Available from: 2012-09-10 Created: 2012-06-20 Last updated: 2012-09-10Bibliographically approved

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