Human gait identification from extremely low-quality videos: an enhanced classifier ensemble method

Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low-quality videos, which have a frame-rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction-based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance-based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model-based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model-based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low-quality versions of OU-ISIR-D and USF databases), and the results suggest that our method is more general and effective than other state-of-the-art algorithms.