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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

February 1, 2023

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Authors

Eric Crawford

Mila/McGill University


Joelle Pineau

Mila/McGill University


DOI:

10.1609/aaai.v34i04.5777


Abstract:

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this will allow agents to begin operating in new environments with minimal human assistance. The task of learning to discover and track objects in videos, which we call unsupervised object tracking, has grown in prominence in recent years; however, most architectures that address it still struggle to deal with large scenes containing many objects. In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). In a series of experiments, we demonstrate a number of attractive features of our architecture; most notably, that it outperforms competing methods at tracking objects in cluttered scenes with many objects, and that it can generalize well to videos that are larger and/or contain more objects than videos encountered during training.

Topics: AAAI

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HOW TO CITE:

Eric Crawford||Joelle Pineau Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking Proceedings of the AAAI Conference on Artificial Intelligence (2020) 3684-3692.

Eric Crawford||Joelle Pineau Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking AAAI 2020, 3684-3692.

Eric Crawford||Joelle Pineau (2020). Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 3684-3692.

Eric Crawford||Joelle Pineau. Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.3684-3692.

Eric Crawford||Joelle Pineau. 2020. Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking. "Proceedings of the AAAI Conference on Artificial Intelligence". 3684-3692.

Eric Crawford||Joelle Pineau. (2020) "Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking", Proceedings of the AAAI Conference on Artificial Intelligence, p.3684-3692

Eric Crawford||Joelle Pineau, "Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking", AAAI, p.3684-3692, 2020.

Eric Crawford||Joelle Pineau. "Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.3684-3692.

Eric Crawford||Joelle Pineau. "Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 3684-3692.

Eric Crawford||Joelle Pineau. Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking. AAAI[Internet]. 2020[cited 2023]; 3684-3692.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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