Multi-level semantic appearance representation for person re-identification system

Fendri, Emna; Frikha, Mayssa; Hammami, Mohamed

doi:10.1016/j.patrec.2017.09.039

Cited by 8 publications

(1 citation statement)

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“…There are many methods use part-based representation to learn discriminative features for person Re-ID. Following the strategy of part partition, the part-based approaches can be roughly divided into two categories: 1) Rigid body part partition, which has been widely adopted in many methods [24,32,16,33], where the authors use predefined rigid grids as local parts. Each part is fed into an individual branch.…”

Section: Related Workmentioning

confidence: 99%

Deep-Person: Learning discriminative deep features for person Re-Identification

Bai

Yang

Huang

et al. 2020

Pattern Recognition

206

118

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Person re-identification (Re-ID) requires discriminative features focusing on the full person to cope with inaccurate person bounding box detection, background clutter, and occlusion. Many recent person Re-ID methods attempt to learn such features describing full person details via part-based feature representation. However, the spatial context between these parts is ignored for the independent extractor on each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the * Corresponding author

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Section: Related Workmentioning

confidence: 99%