atlas

Research map / Video / Video segmentation and tracking

Recurrent Convolutional Network for Video-Based Person Re-Identification

CVPR 2016 · Video › Video segmentation and tracking · approach: Spatial-temporal video person re-identification · 596 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add recurrent connections to the CNN feature sequence (ablation)Video re-identification network · iLIDS-VID and PRID-2011CMCHelped
Add optical-flow channels alongside colour input (ablation)Video re-identification network · iLIDS-VID and PRID-2011CMCHelped
Replace max-pooling with mean-pooling over time (ablation)CNN sequence representation · iLIDS-VIDCMCHelped
Use mean-pooling over the sequence rather than comparing individual frames (ablation)CNN sequence representation · iLIDS-VIDCMCHelped
Increase probe or gallery sequence length (ablation)Video re-identification system · iLIDS-VIDRank 1 CMCHelped
Use full video sequences instead of single images (ablation)Ours versus Ours* · PRID-2011 cross-datasetRank 1 CMC (%): 14 -> 28Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy