# Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition

arXiv: https://arxiv.org/abs/2209.12364 · HTML: https://mlsanity.com/paper/2209.12364/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition](https://mlsanity.com/paper/1711.11152/) (2017)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Representation Flow for Action Recognition](https://mlsanity.com/paper/1810.01455/) (2018)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [Local Binary Convolutional Neural Networks](https://mlsanity.com/paper/1608.06049/) (2016)
- [View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations](https://mlsanity.com/paper/2010.09092/) (2020)
- [Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules](https://mlsanity.com/paper/2010.09084/) (2020)
- [Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs](https://mlsanity.com/paper/2002.02117/) (2020)
- [Sequential convolutional network for behavioral pattern extraction in gait recognition](https://mlsanity.com/paper/2104.11473/) (2021)
- [View-invariant Gait Recognition through Genetic Template Segmentation](https://mlsanity.com/paper/1705.05273/) (2017)

## Built on by (most cited first, 2 of 2)

- [TinySiamese Network for Biometric Analysis](https://mlsanity.com/paper/2307.00578/) (2023)
- [Local Spatiotemporal Convolutional Network for Robust Gait Recognition](https://mlsanity.com/paper/2605.14548/) (2026)

## Abstract (arXiv)

The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed, which can allow the classic convolutional structure to have the capability to directly portray gait movement patterns at the feature level. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly described at various levels of temporal resolution. Furthermore, a correction block is devised to eliminate the segmentation noise of silhouettes for getting more precise gait information. Subsequently, the temporal feature set and the spatial features are combined to comprehensively characterize gait processes. Extensive experiments are conducted on CASIA-B and OU-MVLP datasets, and the results achieve an outstanding identification performance, which has demonstrated the effectiveness of the proposed approach.