Video Human Segmentation using Fuzzy Object Models and its Application to Body Pose Estimation of Toddlers for Behavior Studies
This work addresses a domain-specific problem for autism behavior studies in toddlers, offering an incremental improvement by adapting an existing model to a new application.
The paper tackles video human segmentation for toddlers by extending a fuzzy object model (CSM) to handle articulation and occlusions, applying it to body pose estimation for autism risk assessment, with results showing it provides insightful knowledge to assist specialists in clinical evaluations.
Video object segmentation is a challenging problem due to the presence of deformable, connected, and articulated objects, intra- and inter-object occlusions, object motion, and poor lighting. Some of these challenges call for object models that can locate a desired object and separate it from its surrounding background, even when both share similar colors and textures. In this work, we extend a fuzzy object model, named cloud system model (CSM), to handle video segmentation, and evaluate it for body pose estimation of toddlers at risk of autism. CSM has been successfully used to model the parts of the brain (cerebrum, left and right brain hemispheres, and cerebellum) in order to automatically locate and separate them from each other, the connected brain stem, and the background in 3D MR-images. In our case, the objects are articulated parts (2D projections) of the human body, which can deform, cause self-occlusions, and move along the video. The proposed CSM extension handles articulation by connecting the individual clouds, body parts, of the system using a 2D stickman model. The stickman representation naturally allows us to extract 2D body pose measures of arm asymmetry patterns during unsupported gait of toddlers, a possible behavioral marker of autism. The results show that our method can provide insightful knowledge to assist the specialist's observations during real in-clinic assessments.