Person re-identification using height-based gait in colour depth camera

We address the problem of person re-identification in colour-depth camera using the height temporal information of people. Our proposed gait-based feature corresponds to the frequency response of the height temporal information. We demonstrate that the discriminative periodic motion associated with human gait is encoded within the height temporal information. Additionally, we also investigate the discriminative ability of a novel feature vector obtained by the integration of the height temporal information with a color and height-based appearance model. Given the proposed features we adopt a feature selection scheme for each person, based on KL-divergence, to identify the discriminative subset of frequency bins for the height-based gait feature that enhances the overall identification accuracy. To identify the test person, we formulate a probabilistic matching framework incorporating the selected frequency bins. We validate our algorithm on the publicly available TUM-GAID dataset and our studio datasets and report over 80% accuracy for 75 people with our combined feature, significantly better than standard colour-based features. Additionally, we also observe that height-based gait features reports over 90% for smaller population and are rotation invariant, robust to appearance noise and occlusion.