基于主动行为表达的高效互动识别方法
首发时间:2014-12-24
摘要: 本文提出了一种将两个人的互动分解主动行为和被动行为的新方法以实现更有效的行为识别。在两人的互动中,主动行为起到决定性的作用。因此,互动行为识别可以被简化为只关注一个人的行为表达的主动行为识别。最近,微软公司的一种新型深度传感器Kinect被广泛使用。它可以提供RGB-D数据以及三维空间信息来用作定量分析。然而,那些用于评估两人互动行为识别方法的可公开访问的测试数据集中,很少有是使用此种摄像头获取的。所以,我们创建了一个包含六种复杂人体行为的新数据集K3HI。其中包括踢、指、拳击、推、交换物品和握手。对每一种主动行为提取出其关节、面和速度这三个特征。本文采用自己的数据集通过连续的隐马尔可夫模型(HMMs)来评估主动行为识别方法和传统两人互动行为识别方法。实验结果表明本文提出的识别方法相比于传统方法具有更准确,并且缩短了样本训练时间,从而体现了其综合优势。
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Efficient Interaction Recognition through Positive Action Representation
Abstract:This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI), including kicking, pointing, punching, pushing, exchanging an object, and shaking hands.Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs) to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.
Keywords: pattern recognition interaction recognition Kinect HMMs
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