PolyTracker: Progressive Contour Regression for Multiple Object Tracking and Segmentation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13537 LNCS, Page: 633-645
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
State-of-the-art multiple object tracking and segmentation methods predict a pixel-wise segmentation mask for each detected instance object. Such methods are sensitive to the inaccurate detection and suffer from heavy computational overhead. Besides, when associating pixel-wise masks, additional optical flow networks are required to assist in mask propagation. To relieve these three issues, we present PolyTracker, which adopts object contour, in the form of a vertex sequence along with the object silhouette, as an alternative representation of the pixel-wise segmentation mask. In the PolyTracker, we design an effective contour deformation module based on an iterative and progressive mechanism, which is robust to the inaccurate detection and has low model complexity. Furthermore, benefiting from the powerful contour deformation module, we design a novel data association method, which achieves effective contour propagation by fully mining contextual cues from contours. Since data association relies heavily on pedestrian appearance representation, we design a Reliable Pedestrian Information Aggregation (RPIA) module to fully exploit the discriminative re-identification feature. Extensive experiments demonstrate that our PolyTracker sets the promising records on the MOTS20 benchmark.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85142801593&origin=inward; http://dx.doi.org/10.1007/978-3-031-18916-6_50; https://link.springer.com/10.1007/978-3-031-18916-6_50; https://dx.doi.org/10.1007/978-3-031-18916-6_50; https://link.springer.com/chapter/10.1007/978-3-031-18916-6_50
Springer Science and Business Media LLC
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