Recurrent Neural Networks for Person Re-identification Revisited
This work addresses video-based person re-identification for surveillance and security applications, but it is incremental as it builds on existing RNN methods by simplifying the architecture.
The paper tackled the problem of person re-identification in videos by revisiting Recurrent Neural Networks (RNNs), showing that a simpler feed-forward architecture with similar parameters achieves comparable accuracy and converges faster, with accuracy improvements of up to 5% on two datasets.
The task of person re-identification has recently received rising attention due to the high performance achieved by new methods based on deep learning. In particular, in the context of video-based re-identification, many state-of-the-art works have explored the use of Recurrent Neural Networks (RNNs) to process input sequences. In this work, we revisit this tool by deriving an approximation which reveals the small effect of recurrent connections, leading to a much simpler feed-forward architecture. Using the same parameters as the recurrent version, our proposed feed-forward architecture obtains very similar accuracy. More importantly, our model can be combined with a new training process to significantly improve re-identification performance. Our experiments demonstrate that the proposed models converge substantially faster than recurrent ones, with accuracy improvements by up to 5% on two datasets. The performance achieved is better or on par with other RNN-based person re-identification techniques.