Abstract
Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major challenge for gait recognition as human gait silhouettes are usually different from different view angles. Traditionally, such a multi-view gait recognition problem can be tackled by View Transformation Model (VTM) which transforms gait features from multiple gallery views to the probe view so as to evaluate the gait similarity. In the real-world environment, however, gait sequences may be captured from an uncontrolled scene and the view angle is often unknown, dynamically changing, or does not belong to any predefined views (thus VTM becomes inapplicable). To address this free-view gait recognition problem, we propose an innovative view-adaptive mapping (VAM) approach. The VAM employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation. Additionally, a RankSVM-based algorithm is developed to supplement the gallery data for subjects whose gallery features are only available in predefined views. Extensive experiments on both indoor and outdoor datasets demonstrate that the VAM outperforms several reference methods remarkably in free-view gait recognition.
Highlights
In recent years, surveillance cameras have been widely deployed in many cities
We develop a RankSVM-based algorithm that supplements gait data when gallery features are only available in certain predefined views
This validates the effectiveness of the proposed joint gait manifold (JGM), and shows that choosing the closest view to the probe data for view transformation [20] is not optimal
Summary
Surveillance cameras have been widely deployed in many cities. To automatically analyze the data captured from these cameras (e.g., for searching for a suspicious person or vehicle), different biometric technologies have been developed and playing more and more important roles in public security applications and crime investigation. Human gait may be affected by various factors in practical visual surveillance scenes, e.g. change in view angles, variation of walking speed, carrying an object and even wearing different types of shoes [3]. Among all these factors, change in view angles is regarded as one of the most common challenges as it often changes the visual features significantly (e.g., visible body parts, global shape statistics, and walking trajectories [4, 5]).
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