@inproceedings{1bf82364613b4ac1bbe7f5142576a969,
title = "Depth Masked Discriminative Correlation Filter",
abstract = "Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications. In this work, we propose a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel depth segmentation based occlusion detection that stops correlation filter updating and depth masking which adaptively adjusts the spatial support for correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among the state-of-the-art in overall ranking and the winner on multiple categories. Moreover, since it is based on DCF, “DM-DCF” runs an order of magnitude faster than its competitors making it suitable for time constrained applications.",
keywords = "correlation methods, image colour analysis, image filtering, image segmentation, object detection, object tracking, correlation filter, occlusion detection, Princeton RGBD Tracking Benchmark, depth segmentation, DM-DCF, Depth Masked Discriminative Correlation Filter, Target tracking, Correlation, Three-dimensional displays, Object tracking, Benchmark testing, Two dimensional displays, Information filtering",
author = "Uğur Kart and Joni-Kristian K{\"a}m{\"a}r{\"a}inen and Ji{\v r}{\'i} Matas and Lixin Fan and Francesco Cricri",
note = "EXT={"}Matas, Ji{\v r}{\'i}{"} EXT={"}Cricri, Francesco{"}; International Conference on Pattern Recognition ; Conference date: 01-01-1900",
year = "2018",
month = aug,
doi = "10.1109/ICPR.2018.8546179",
language = "English",
isbn = "978-1-5386-3789-0",
publisher = "IEEE",
pages = "2112--2117",
booktitle = "2018 24th International Conference on Pattern Recognition (ICPR)",
address = "United States",
}