Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction.
Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras.
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.
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.
With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated.
The goal is to identify a person from videos captured under different cameras.
The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest.
SOTA for Person Re-Identification on PRID2011 (using extra training data)
Person re-identification (Re-ID) is an important real-world surveillance problem that entails associating a person's identity over a network of cameras.