Automatic learning of gait signatures for people identification
It addresses gait-based identification for security or surveillance applications, but is incremental as it applies CNNs to an existing approach.
This work tackled people identification from gait in video by using convolutional neural networks (CNN) to learn descriptors from optical flow, achieving state-of-the-art results on the TUM-GAID dataset with an image resolution eight times lower than previous methods (80x60 pixels).
This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).