Motion Tracking in Video using the Best Feature Extraction Technique
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Master's Thesis from the year 2009 in the subject Computer Science - Applied, grade: Distinction, University of Essex, course: MSc Computer Science, language: English, abstract: This project explores the use of local features for motion tracking to estimate the direction of a moving object. Availability of a number of feature extraction and matching algorithms in literature make it difficult to select any one of them. Therefore, it was considered appropriate to assess the suitability of a technique for a particular application before actually putting it into use. The project begins with a comp...
Master's Thesis from the year 2009 in the subject Computer Science - Applied, grade: Distinction, University of Essex, course: MSc Computer Science, language: English, abstract: This project explores the use of local features for motion tracking to estimate the direction of a moving object. Availability of a number of feature extraction and matching algorithms in literature make it difficult to select any one of them. Therefore, it was considered appropriate to assess the suitability of a technique for a particular application before actually putting it into use. The project begins with a comparative study and analyzes two state-of-art techniques for feature extraction (SIFT and SURF) along with two best known matching algorithms (RANSAC and Hough Transform). The performance evaluation is focused on measuring the correctness of the algorithms for tracking applications. The statistics Mc Nemar s test has been applied to find the more efficient method for feature extraction and matching. Using the results obtained from this analysis, a set of feature extractor and matching technique has been employed to estimate the direction of a moving object from videos captured using a handheld camera as well as camera fixed on a moving vehicle. The proposed method is capable of detecting left, right, up, and down movements with reasonable accuracy in real world videos. The results are not hundred percent accurate but encouraging enough for further investigation. The system is capable of identifying the direction of the moving object with more than 90% accuracy if the object changes its direction independent of the surroundings, and with less than 30% accuracy otherwise.
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