Sequential convolutional network for behavioral pattern extraction in gait recognition

TL;DR

This paper introduces a sequential convolutional network (SCN) that effectively extracts behavioral gait patterns from video sequences by learning spatiotemporal features, surpassing existing methods on public benchmarks.

Contribution

The paper proposes a novel SCN architecture with behavioral information extractors and multi-frame aggregation, improving gait recognition accuracy over prior approaches.

Findings

Superior performance on CASIA-B and OU-MVLP benchmarks

Effective extraction of walking patterns from sequences

Outperforms state-of-the-art methods

Abstract

As a unique and promising biometric, video-based gait recognition has broad applications. The key step of this methodology is to learn the walking pattern of individuals, which, however, often suffers challenges to extract the behavioral feature from a sequence directly. Most existing methods just focus on either the appearance or the motion pattern. To overcome these limitations, we propose a sequential convolutional network (SCN) from a novel perspective, where spatiotemporal features can be learned by a basic convolutional backbone. In SCN, behavioral information extractors (BIE) are constructed to comprehend intermediate feature maps in time series through motion templates where the relationship between frames can be analyzed, thereby distilling the information of the walking pattern. Furthermore, a multi-frame aggregator in SCN performs feature integration on a sequence whose length is uncertain, via a mobile 3D convolutional layer. To demonstrate the effectiveness, experiments have been conducted on two popular public benchmarks, CASIA-B and OU-MVLP, and our approach is demonstrated superior performance, comparing with the state-of-art methods.