# Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding

## TL;DR

This paper introduces a two-stream convolutional network with co-attentive embedding for video-based person re-identification, effectively utilizing multiple data modalities and snippet aggregation to improve accuracy.

## Contribution

It presents a novel two-stream ConvNet with co-attentive embedding and competitive snippet aggregation, enhancing robustness and performance in video-based person re-identification.

## Key findings

- Significantly outperforms existing methods on multiple datasets.
- Effectively reduces noise impact through co-attentive embedding.
- Utilizes RGB, optical flow, and pose maps for comprehensive feature extraction.

## Abstract

Recently, the applications of person re-identification in visual surveillance and human-computer interaction are sharply increasing, which signifies the critical role of such a problem. In this paper, we propose a two-stream convolutional network (ConvNet) based on the competitive similarity aggregation scheme and co-attentive embedding strategy for video-based person re-identification. By dividing the long video sequence into multiple short video snippets, we manage to utilize every snippet's RGB frames, optical flow maps and pose maps to facilitate residual networks, e.g., ResNet, for feature extraction in the two-stream ConvNet. The extracted features are embedded by the co-attentive embedding method, which allows for the reduction of the effects of noisy frames. Finally, we fuse the outputs of both streams as the embedding of a snippet, and apply competitive snippet-similarity aggregation to measure the similarity between two sequences. Our experiments show that the proposed method significantly outperforms current state-of-the-art approaches on multiple datasets.