Fast object tracking system is important to Augmented Reality (AR). However, tracking multiple objects fast and accurately is not a trivial task in computervision, especially when the objects become crowded and frequ...
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ISBN:
(数字)9781665453653
ISBN:
(纸本)9781665453653
Fast object tracking system is important to Augmented Reality (AR). However, tracking multiple objects fast and accurately is not a trivial task in computervision, especially when the objects become crowded and frequently occluded. In this paper, we design an efficient segmentation model along with the tracking and carefully study the contributions of pixel-level appearance modeling in the presence of occlusions. Different from previous joint tracking and segmentation methods that depend on bounding boxes for instance segmentation, we proposed an anchor free model to speed the object mask estimation. Specifically, our proposed approach jointly addresses instance segmentation and tracking with a single convolutional network for the first time, and generates object masks to explicitly separate overlapping objects in tracking. As pointed out by several previous works, the goals of tracking and segmentation are not always consistent, we deliberately design three homogeneous branches in our network to balance the competitive three functional parts of objectness, masks, and re-identification simultaneously and constrain them in a comparable scale. With the effective three functional parts, our method could run fast for the time-sensitive tracking task and meanwhile relieve the effects of appearance noise brought by the occlusions. Experimental results on the MOTS benchmark demonstrate the effectiveness of the proposed method.
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