CASIA OpenIR  > 智能系统与工程
Temporal-adaptive sparse feature aggregation for video object detection
He, Fei1,2; Li, Qiaozhe1,2; Zhao, Xin1,2; Huang, Kaiqi1,2,3
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2022-07-01
Volume127Pages:10
Corresponding AuthorZhao, Xin(xzhao@nlpr.ia.ac.cn)
AbstractVideo object detection is a challenging task due to the appearance deterioration in video frames. To enhance feature representation of the deteriorated frames, previous methods usually aggregate features from fixed-density and fixed-length adjacent frames. However, due to the redundancy of videos and irregular object movements over time, temporal information may not be efficiently exploited using the traditional inflexible strategy. Alternatively, we present a temporal-adaptive sparse feature aggregation framework, an accurate and efficient method for video object detection. Instead of adopting a fixed-density and fixed-length window fusion strategy, a temporal-adaptive sparse sampling strategy is proposed using a stride predictor to encode informative frames more efficiently. A collaborative feature aggregation framework, which consists of a pixel-adaptive aggregation module and an object-relational aggregation module, is proposed for feature enhancement. The pixel-adaptive aggregation module enhances pixel level features on the current frame using corresponding pixel-level features from other frames. Similarly, the object-relational aggregation module further enhances feature representation at proposal level. A graph is constructed to model the relations between different proposals so that the relation features and proposal features are adaptively fused for feature enhancement. Experiments demonstrate that our proposed framework significantly surpasses traditional dense aggregation methods, and comprehensive ablation studies verify the effectiveness of each proposed module in our framework.
KeywordVideo object detection Temporal-adaptive sparse sampling Pixel-adaptive aggregation Object-relational aggregation
DOI10.1016/j.patcog.2022.108587
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61876181] ; Projects of Chinese Academy of Science[QYZDB-SSW-JSC006] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27000000] ; Youth Innovation Promotion Association CAS
Funding OrganizationNational Natural Science Foundation of China ; Projects of Chinese Academy of Science ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000776971700003
PublisherELSEVIER SCI LTD
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48302
Collection智能系统与工程
Corresponding AuthorZhao, Xin
Affiliation1.Chinese Acad Sci, Inst Automat, CRISE, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
He, Fei,Li, Qiaozhe,Zhao, Xin,et al. Temporal-adaptive sparse feature aggregation for video object detection[J]. PATTERN RECOGNITION,2022,127:10.
APA He, Fei,Li, Qiaozhe,Zhao, Xin,&Huang, Kaiqi.(2022).Temporal-adaptive sparse feature aggregation for video object detection.PATTERN RECOGNITION,127,10.
MLA He, Fei,et al."Temporal-adaptive sparse feature aggregation for video object detection".PATTERN RECOGNITION 127(2022):10.
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