Abstract
Based on the approximation that tracklet kinematic association likelihoods satisfy the Markov or path-independence assumption, several polynomial-time bipartite matching algorithms were proposed to stitch track segments for their effectiveness. However, with target density increasing, their stitching performance would degrade inevitably. Despite the help of feature information, it is remarkable that the aforementioned approximation is no longer valid since the feature information is usually sporadic. In order to solve this problem, track graph is utilized and the feature information is passed through the graph to calculate the tracklet feature association likelihood under path-dependence assumption. It makes bipartite matching algorithms valid again. Finally, simulation results demonstrate that the proposed algorithm outperforms previous algorithms based on path-independence assumption in the dense target situation.
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This work was supported in part by the National Natural Science Foundation of China (61471019).
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Fu, J., Sun, J., Lei, P. (2018). Graph-Based Tracklet Stitching with Feature Information for Ground Target Tracking. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_57
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DOI: https://doi.org/10.1007/978-981-13-0893-2_57
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