在计算机视觉和多媒体领域,利用视觉信息进行语义层面人体运动分析非常重要且具有挑战性.提出一种利用检测信息的底层响应来描述人体动作的语义信息方法.在特定的人体动作下,可变形部分模型的检测结果隐含人体部分的关键信息,可以形成人体动作识别的特征.利用检测器的滤波器响应生成人体描述特征,对人体整体和部分的位置以及表观信息进行编码,由于该特征利用了人体部分相对于整体位置的统计信息,对检测过程中的误检部分具有较强的鲁棒性,基于该特征可将人体检测和动作识别融合成统一框架.在3个数据库上的实验结果显示了方法的有效性,取得了与其他方法相近或者更优的效果.

In computer vision and multimedia areas, it's an important yet challenging problem to perceive human motion at semantic level. In this work, a novel approach is presented to map the low-level response to semantic description of human actions. The features are based on the detection of deformable part models, in which the body pose information is contained implicitly under the specific human actions. The filter responses of the detectors are mapped to an effective feature description, which encodes the position and appearance information of human body and parts. The obtained features capture the relative configuration of body parts, and are robust to the false detections occurred in the individual part detectors. Comprehensive experiments conducted on three databases show the presented method achieves remarkable performance in most of the cases.

国家自然科学基金(61273285, 61375019);国家重点基础研究发展计划(973)(2011CB30220)