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Cross-View Gait Recognition with Short Probe Sequences : From View Transformation Model to View-Independent Stance-Independent Identity Vector
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang
Source PublicationInternational Journal of Pattern Recognition and Artificial Intelligence
2013-09-11
Volume27Issue:6Pages:1-17
AbstractConsidering it is difficult to guarantee that at least one continuous complete gait cycle is captured in real applications, we address the multi-view gait recognition problem with short probe sequences. With unified multi-view population hidden markov models (umvpHMMs), the gait pattern is represented as fixed-length multi-view stances. By incorporating the multi-stance dynamics, the well-known view transformation model (VTM) is extended into a multi-linear projection model in a four-order tensor space, so that a view-independent stance-independent identity vector (VSIV) can be extracted. The main advantage is that the proposed VSIV is stable for each subject regardless of the camera location or the sequence length. Experiments show that our algorithm achieves encouraging performance for cross-view gait recognition even with short probe sequences.
KeywordView-independent Stance-independent Gait Recognition Multi-stance Dynamics Short Probe Sequence
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13221
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang. Cross-View Gait Recognition with Short Probe Sequences : From View Transformation Model to View-Independent Stance-Independent Identity Vector[J]. International Journal of Pattern Recognition and Artificial Intelligence,2013,27(6):1-17.
APA Maodi Hu,Yunhong Wang,&Zhaoxiang Zhang.(2013).Cross-View Gait Recognition with Short Probe Sequences : From View Transformation Model to View-Independent Stance-Independent Identity Vector.International Journal of Pattern Recognition and Artificial Intelligence,27(6),1-17.
MLA Maodi Hu,et al."Cross-View Gait Recognition with Short Probe Sequences : From View Transformation Model to View-Independent Stance-Independent Identity Vector".International Journal of Pattern Recognition and Artificial Intelligence 27.6(2013):1-17.
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