Integrating Grammar and Segmentation for Human Pose Estimation
In this paper we present a compositional and-or graph grammar model for human pose estimation. Our model has three distinguishing features: (i) large appearance differences between people are handled compositionally by allowing parts or collections of parts to be substituted with alternative variants, (ii) each variant is a sub-model that can define its own articulated geometry and context-sensitive compatibility with neighboring part variants, and (iii) background region segmentation is incorporated into the part appearance models to better estimate the contrast of a part region from its surroundings, and improve resilience to background clutter. The resulting integrated framework is trained discriminatively in a max-margin framework using an efficient and exact inference algorithm. We present experimental evaluation of our model on two popular datasets, and show performance improvements over the state-of-art on both benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Pose EstimationSimilar Papers 제목 키워드 기반
Predicting Human Activities Using Stochastic Grammar
This paper presents a novel method to predict future human activities from partially observed RGB-D videos. Human activity prediction is generally difficult due to its non-Markovian property and the rich context between …
Activity PredictionBlended Grammar Network for Human Parsing
Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGN…
Human ParsingAttribute And-Or Grammar for Joint Parsing of Human Attributes, Part and Pose
This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast…
AttributeHuman DetectionPose EstimationDetection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle
Human pose estimation methods work well on separated people but struggle with multi-body scenarios. Recent work has addressed this problem by conditioning pose estimation with detected bounding boxes or bottom-up-estimat…
Human Instance SegmentationPose-Based Human Instance SegmentationPose EstimationSegmentationHuman Action Forecasting by Learning Task Grammars
For effective human-robot interaction, it is important that a robotic assistant can forecast the next action a human will consider in a given task. Unfortunately, real-world tasks are often very long, complex, and repeti…
Action RecognitionTemporal Action Localization