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Title: Motion context network for weakly supervised object detection in videos
Authors: Jin, Ruibing
Lin, Guosheng
Wen, Changyun
Wang, Jianliang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Jin, R., Lin, G., Wen, C. & Wang, J. (2020). Motion context network for weakly supervised object detection in videos. IEEE Signal Processing Letters, 27, 1864-1868.
Project: RG28/18 (S)
RG22/19 (S)
Journal: IEEE Signal Processing Letters
Abstract: In weakly supervised object detection, most existing approaches are proposed for images. Without box-level annotations, these methods cannot accurately locate objects. Considering an object may show different motion from its surrounding objects or background, we leverage motion information to improve the detection accuracy. However, the motion pattern of an object is complex. Different parts of an object may have different motion patterns, which poses challenges in exploring motion information for object localization. Directly using motion information may degrade the localization performance. To overcome these issues, we propose a Motion Context Network (MC-Net) in this letter. Ourmethod generatesmotion context features by exploiting neighborhood motion correlation information on moving regions. These motion context features are then incorporated with image information to improve the detection accuracy. Furthermore, we propose a temporal aggregation module, which aggregates features across frames to enhance the feature representation at the current frame. Experiments are carried out on ImageNet VID, which shows that our MC-Net significantly improves the performance of the image based baseline method (37.4% mAP v.s. 29.8% mAP).
ISSN: 1070-9908
DOI: 10.1109/LSP.2020.3029958
Schools: School of Electrical and Electronic Engineering 
School of Computer Science and Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

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