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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 1: AAAI-22 Technical Tracks 1

ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation

February 1, 2023

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Authors

Jinze Chen

University of Science and Technology of China


Yang Wang

University of Science and Technology of China


Yang Cao

University of Science and Technology of China Institute of Artificial Intelligence, Hefei Comprehensive National Science Center


Feng Wu

University of Science and Technology of China


Zheng-Jun Zha

University of Science and Technology of China


DOI:

10.1609/aaai.v36i1.19906


Abstract:

Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields. However, the output event stream of existing DVS inevitably contains background activity noise (BA noise) due to dark current and junction leakage current, which will affect the temporal correlation of objects, resulting in deteriorated motion estimation performance. Particularly, the existing filter-based denoising methods cannot be directly applied to suppress the noise in event stream, since there is no spatial correlation. To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner. Specifically, based on the maximum sharpness criterion, ME module divides the input event into several segments by adaptive clustering in a motion compensating warp field, and captures the temporal correlation of event stream according to the clustered motion parameters. Taking temporal correlation as guidance, ED module calculates the confidence that each event belongs to real activity events, and transmits it to ME module to update energy function of motion segmentation for noise suppression. The two steps are iteratively updated until stable motion segmentation results are obtained. Extensive experimental results on both synthetic and real datasets demonstrate the superiority of our proposed approaches against the State-Of-The-Art (SOTA) methods.

Topics: AAAI

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HOW TO CITE:

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation AAAI 2022, 303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha (2022). ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. 2022. ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. (2022) "ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.303-311

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha, "ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation", AAAI, p.303-311, 2022.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. "ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. "ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 303-311.

Jinze Chen||Yang Wang||Yang Cao||Feng Wu||Zheng-Jun Zha. ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation. AAAI[Internet]. 2022[cited 2023]; 303-311.


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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