Automatic learning of gait signatures for people identification
This work targets people identification in video based on the way they walk (ie 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 (ie 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 …
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 (ie 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 …
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