Abstract
Real time pedestrian tracking could be one of the important features for autonomous navigation. Laser Range Finder (LRF) produces accurate pedestrian data but a problem occurs when a pedestrian is represented by multiple clusters which affect the overall tracking process. Multiple Hypothesis Tracking (MHT) is a proven method to solve tracking problem but suffers a large computational cost. In this paper, a multilevel clustering of LRF data is proposed to improve the accuracy of a tracking system by adding another clustering level after the feature extraction process. A Dynamic Track Management (DTM) is introduced in MHT with multiple motion models to perform a track creation, association, and deletion. The experimental results from real time implementation prove that the proposed multiclustering is capable of producing a better performance with less computational complexity for a track management process. The proposed Dynamic Track Management is able to solve the tracking problem with lower computation time when dealing with occlusion, crossed track, and track deletion.
Highlights
Detection and tracking moving object system (DATMO) is one of the most important and popular research areas in autonomous navigation
When dealing with crowded environment or urban area, a DATMO system tends to face difficulties to deal with noisy data provided by Laser Range Finder (LRF)
The developed DATMO algorithm with the Dynamic Track Management (DTM)-Multiple Hypothesis Tracking (MHT) was evaluated on its capability to deal with various important situations in tracking part
Summary
Detection and tracking moving object system (DATMO) is one of the most important and popular research areas in autonomous navigation. DATMO is widely used for system monitoring using camera either in indoor or outdoor usage under both static and dynamic environments but it is unable to provide accurate measurements for long distance objects. The presence of Laser Range Finder (LRF) which is capable of providing accurate range information, wide coverage area, and a low time interval permits implementations in real time system. Pedestrian tracking in urban area is one of the useful implementations of DATMO for autonomous navigation. When dealing with crowded environment or urban area, a DATMO system tends to face difficulties to deal with noisy data provided by LRF. In order to realize DATMO in real time implementation, a suitable optimization is needed especially in computational time and detection rate
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