SoDA: Multi-Object Tracking with Soft Data Association Article Swipe

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Wei-Chih Hung , Henrik Kretzschmar , Tsung-Yi Lin , Yuning Chai , Ruichi Yu , Ming–Hsuan Yang , Dragomir Anguelov ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2008.07725

Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars. Tracking objects, however, remains a highly challenging problem, especially in cluttered autonomous driving scenes in which objects tend to interact with each other in complex ways and frequently get occluded. We propose a novel approach to MOT that uses attention to compute track embeddings that encode the spatiotemporal dependencies between observed objects. This attention measurement encoding allows our model to relax hard data associations, which may lead to unrecoverable errors. Instead, our model aggregates information from all object detections via soft data associations. The resulting latent space representation allows our model to learn to reason about occlusions in a holistic data-driven way and maintain track estimates for objects even when they are occluded. Our experimental results on the Waymo OpenDataset suggest that our approach leverages modern large-scale datasets and performs favorably compared to the state of the art in visual multi-object tracking.

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Computer Science

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Computer science ENCODE Object (grammar) Artificial intelligence Association (psychology) Computer vision Representation (politics) Tracking (education) Track (disk drive) Video tracking Encoding (memory) Software deployment Gene Pedagogy Politics Political science Chemistry Operating system Biochemistry Epistemology Philosophy Psychology Law

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2008.07725
PDF: https://arxiv.org/pdf/2008.07725
OA Status: green
Cited By: 6
References: 51
Related Works: 10
OpenAlex ID: https://openalex.org/W3073032883

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DOI: https://doi.org/10.48550/arxiv.2008.07725

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Title: SoDA: Multi-Object Tracking with Soft Data Association

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-08-18

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Authors: Wei-Chih Hung, Henrik Kretzschmar, Tsung-Yi Lin, Yuning Chai, Ruichi Yu, Ming–Hsuan Yang, Dragomir Anguelov

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Landing page: https://arxiv.org/abs/2008.07725

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PDF URL: https://arxiv.org/pdf/2008.07725

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Concepts: Computer science, ENCODE, Object (grammar), Artificial intelligence, Association (psychology), Computer vision, Representation (politics), Tracking (education), Track (disk drive), Video tracking, Encoding (memory), Software deployment, Gene, Pedagogy, Politics, Political science, Chemistry, Operating system, Biochemistry, Epistemology, Philosophy, Psychology, Law

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Cited by: 6

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Citations by year (recent): 2024: 1, 2023: 1, 2022: 1, 2021: 3

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References (count): 51

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abstract_inverted_index.novel	46
abstract_inverted_index.other	35
abstract_inverted_index.relax	73
abstract_inverted_index.space	99
abstract_inverted_index.state	147
abstract_inverted_index.track	55, 117
abstract_inverted_index.which	28, 77
abstract_inverted_index.Robust	0
abstract_inverted_index.allows	69, 101
abstract_inverted_index.encode	58
abstract_inverted_index.highly	18
abstract_inverted_index.latent	98
abstract_inverted_index.modern	138
abstract_inverted_index.object	90
abstract_inverted_index.reason	107
abstract_inverted_index.scenes	26
abstract_inverted_index.visual	152
abstract_inverted_index.between	62
abstract_inverted_index.complex	37
abstract_inverted_index.compute	54
abstract_inverted_index.driving	25
abstract_inverted_index.errors.	82
abstract_inverted_index.objects	29, 120
abstract_inverted_index.propose	44
abstract_inverted_index.remains	16
abstract_inverted_index.results	128
abstract_inverted_index.suggest	133
abstract_inverted_index.Instead,	83
abstract_inverted_index.Tracking	13
abstract_inverted_index.approach	47, 136
abstract_inverted_index.compared	144
abstract_inverted_index.datasets	140
abstract_inverted_index.encoding	68
abstract_inverted_index.holistic	112
abstract_inverted_index.however,	15
abstract_inverted_index.interact	32
abstract_inverted_index.maintain	116
abstract_inverted_index.objects,	14
abstract_inverted_index.objects.	64
abstract_inverted_index.observed	63
abstract_inverted_index.performs	142
abstract_inverted_index.problem,	20
abstract_inverted_index.tracking	2
abstract_inverted_index.attention	52, 66
abstract_inverted_index.cluttered	23
abstract_inverted_index.estimates	118
abstract_inverted_index.favorably	143
abstract_inverted_index.leverages	137
abstract_inverted_index.occluded.	42, 125
abstract_inverted_index.resulting	97
abstract_inverted_index.tracking.	154
abstract_inverted_index.aggregates	86
abstract_inverted_index.autonomous	24
abstract_inverted_index.deployment	9
abstract_inverted_index.detections	91
abstract_inverted_index.embeddings	56
abstract_inverted_index.especially	21
abstract_inverted_index.frequently	40
abstract_inverted_index.occlusions	109
abstract_inverted_index.OpenDataset	132
abstract_inverted_index.challenging	19
abstract_inverted_index.data-driven	113
abstract_inverted_index.information	87
abstract_inverted_index.large-scale	139
abstract_inverted_index.measurement	67
abstract_inverted_index.dependencies	61
abstract_inverted_index.experimental	127
abstract_inverted_index.multi-object	1, 153
abstract_inverted_index.prerequisite	6
abstract_inverted_index.self-driving	11
abstract_inverted_index.associations,	76
abstract_inverted_index.associations.	95
abstract_inverted_index.unrecoverable	81
abstract_inverted_index.representation	100
abstract_inverted_index.spatiotemporal	60
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
citation_normalized_percentile