|Title:||Exploiting Temporal Coherence to Improve Person Re-identification||Authors:||Santana, Oliverio J.
|UNESCO Clasification:||1203 Ciencia de los ordenadores||Keywords:||Computer Vision
|Issue Date:||2023||Publisher:||Springer||Journal:||Lecture Notes in Computer Science||Conference:||11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022)||Abstract:||The uncontrolled characteristics of long-term scenarios, like ultra-running competitions, are challenging for person re-identification approaches based on computer vision methods. State-of-the-art techniques have reported hardly moderate success for whole-body runner re-identification due to the existence of distinct illumination conditions, as well as changes of clothing and/or accessories like backpacks, caps, and sunglasses. This paper explores integrating these biometric cues with the particular spatio-temporal context information present in the competition live track system. Our results confirm the significance of this strategy to limit the gallery size and boost re-identification performance.||URI:||http://hdl.handle.net/10553/121566||ISBN:||9783031245374||ISSN:||0302-9743||DOI:||10.1007/978-3-031-24538-1_7||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 13822 LNCS, p. 134-151, (Enero 2023)|
|Appears in Collections:||Actas de congresos|
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