Video-Based Person Re-Identification
26 papers with code • 0 benchmarks • 6 datasets
These leaderboards are used to track progress in Video-Based Person Re-Identification
Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch.
Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video.
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction.
In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.
How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem.
Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification
Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras.