Abstract
This paper proposes an improved Mask RCNN and LMB algorithm for target detection and tracking in complex backgrounds, which follows the architecture of Detect-Before-Track. First, the novel algorithm adopts a two-stage neural network to improve the target positions' accuracy during the detection. Then, the label multi-Bernoulli filter, which is suitable for scenarios with an unknown number of targets and target intersection, is utilized to generate the multiple-target trajectories during the tracking stage. Experiments suggest the algorithm's effectiveness and superiority in complex backgrounds' infrared target detection and tracking.
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