该工作提出了一种新颖的方法来恢复视频序列中手语的部分手势。通过累积编码不同时间段，从外观流原语中提取动力学信息。对于每个新视频，在不同时间间隔上获得累积形状差异（SD-VLAD）表示，以恢复手势的平均和方差运动信息。随着视频的进行，每个部分表示被映射到支持向量机模型中，以实现在线手势识别。在64个类别的公共数据集上评估，该方法能够在仅使用20％的信息时达到53.8％的平均准确率，而使用60％的信息时则达到80％的准确率。对于完整序列，平均准确率为85％。
摘要：
On-line prediction of sign language gestures is nowadays a fundamental task to help and support multimedia interpretation of deaf communities. This work presents a novel approach to recover partial sign language gestures by cumulative coding different intervals of the video sequences. The method starts by computing volumetric patches that contain kinematic information from different appearance flow primitives. Then, several sequential intervals are learned to carry out the task of partial recognition. For each new video, a cumulative shape difference (SD)-VLAD representation is obtained at different intervals of the video. Each SD-VLAD descriptor recovers mean and variance motion information as signature of the computed gesture. Along the video, each partial representation is mapped to a support vector machine model to obtain a gesture recognition, being usable in on-line scenarios. The proposed approach was evaluated in a public dataset with 64 different classes, recorded in 3200 samples. This approach is able to recognize sign gestures using only 20% of the sequence with an average accuracy of 53.8% and with 60% of information, the 80% of accuracy was achieved. For complete sequences the proposed approach achieves 85% on average.
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