OUMVLP-OF: Multi-View Large Population Gait Database With Dense Optical Flow and Its Performance Evaluation

Allam Shehata, Francisco M. Castro, Nicolás Guil, Manuel J. Marín-Jiménez, Yasushi Yagi

Abstract: This paper introduces OUMVLP-OF, a large-scale dataset that provides dense optical flow data derived from the world’s largest multi-view gait database. Unlike conventional gait datasets that primarily rely on binary silhouettes, OUMVLP-OF offers a motion-focused representation that captures fine-grained gait dynamics, enabling a richer and more detailed analysis of human movement. This way, OUMVLP-OF bridges the gap between silhouette-based and motion-based approaches, facilitating the development of novel gait recognition models that leverage both spatial and temporal information. The dataset comprises 10,307 subjects (5,114 males and 5,193 females), with gait sequences recorded from 14 distinct view angles, spanning from 0° to 270° in 15° intervals.A key novelty of OUMVLP-OF lies in its dual-version design: OF-V1, which preserves raw background information to encourage models to focus on subtle gait details while benefiting from natural variability as implicit data augmentation, and OF-V2, which features noise-free optical flow maps, isolating gait dynamics for precise motion analysis. This enables researchers to systematically study the impact of background noise on optical flow-based gait recognition and design more robust recognition models. By introducing the first large-scale optical flow dataset for gait analysis with both noisy and clean versions, OUMVLP-OF provides a unique benchmark for motion-based gait recognition. It advances the state of the art by enabling more robust cross-view recognition, better occlusion handling, and improved generalization across varied environments. To promote further research, OUMVLP-OF will be made publicly available at http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitLPOF.html

External IDs:dblp:journals/access/ShehataCGMY25