Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time

Learning a Pose Lexicon for Semantic Action Recognition

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia

Motion Segment Decomposition of RGB-D Sequences for Human Behavior Understanding

International audienceIn this paper, we propose a framework for analyzing and understanding human behavior from depth videos. The proposed solution first employs shape analysis of the human pose across time to decompose the full motion into short temporal segments representing elementary motions. Then, each segment is characterized by human motion and depth appearance around hand joints to describe the change in pose of the body and the interaction with objects. Finally , the sequence of temporal segments is modeled through a Dynamic Naive Bayes classifier, which captures the dynamics of elementary motions characterizing human behavior. Experiments on four challenging datasets evaluate the potential of the proposed approach in different contexts, including gesture or activity recognition and online activity detection. Competitive results in comparison with state of the art methods are reported