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Semi-Online Multiple Object Tracking Using Graphical Tracklet Association
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DOI：10.1109/LSP.2018.2872403.png)
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摘要
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Online multiple object tracking (MOT) is highly challenging when multiple objects have similar appearance or under long occlusion. In this letter, we propose a semi-online MOT method using online discriminative appearance learning and tracklet association with a sliding window. We connect similar detections of neighboring frames in a temporal window, and improve the performance of appearance feature by online discriminative appearance learning. Then, tracklet association is performed by minimizing a subgraph decomposition cost. Occlusions and missing detections are recovered after tracklet stitching. Our method has been tested on two public datasets. Experimental results have demonstrated the significant performance improvement of our method. Specifically, the proposed method is improved by 831% and 12.38% in terms of Multiple Object Tracking Accuracy and Multiple Object Tracking Precision, respectively, as compared to the baseline.
Keyword:
Semi-online
subgraph decomposition
tracklet association
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9.6
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1.7W
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