Gait Curves for Human Recognition, Backpack Detection and Silhouette Correction in a Nighttime Environment

The need for an automated surveillance system is pronounced at night when the capability of the human eye to detect anomalies is reduced. While there have been significant efforts in the classification of individuals using human metrology and gait, the majority of research assumes a day-time environment. The aim of this study is to move beyond traditional image acquisition modalities and explore the issues of object detection and human identification at night. To address these issues, a spatiotemporal gait curve that captures the shape dynamics of a moving human silhouette is employed. Initially proposed by Wang et al., this representation of the gait is expanded to incorporate modules for individual classification, backpack detection, and silhouette restoration. Evaluation of these algorithms is conducted on the CASIA Night Gait Database, which includes 10 video sequences for each of 153 unique subjects. The video sequences were captured using a low resolution thermal camera. Matching performance of the proposed algorithms is evaluated using a nearest neighbor classifier. The outcome of this work is an efficient algorithm for backpack detection and human identification, and a basis for further study in silhouette enhancement.