Adaptive appearance learning for human pose estimation

We address the problem of pose estimation in videos. The part detectors play important roles, but traditional template-based detectors (e.g. Histogram of Gradient, HoG) fail at pose estimation due to the high variability in appearance. We present an adaptive representation of appearance and shape for articulated human body. The full representation of human body is based on the flexible mixture-of-parts model. We train a Naive Bayes classifier to obtain a confidence score of estimated pose by the basic mixture model, and based on the confidence we learn an instance-specific appearance model. For between-frame consistency, we design a time-efficient energy function for motion cues instead of complex motion models. We incorporate these models into a framework that allows for efficient inference. Quantitative evaluation of pose estimation conducted on two video datasets demonstrates the effectiveness of the proposed method.