A Unified Framework For 3d Hand Tracking

Discriminative techniques are good for hand part detection, however they fail due to sensor noise and high inter-finger occlusion. Additionally, these techniques do not incorporate any kinematic or temporal constraints. Even though model-based descriptive (for example Markov Random Field) or generative (for example Hidden Markov Model) techniques utilize kinematic and temporal constraints well, they are computationally expensive and hardly recover from tracking failure. This paper presents a unified framework for 3D hand tracking, utilizing the best of both methodologies. Hand joints are detected using a regression forest, which uses an efficient voting technique for joint location prediction. The voting distributions are multimodal in nature; hence, rather than using the highest scoring mode of the voting distribution for each joint separately, we fit the five high scoring modes of each joint on a tree-structure Markovian model along with kinematic prior and temporal information. Experimentally, we observed that relying on discriminative technique (i.e. joints detection) produces better results. We therefore efficiently incorporate this observation in our framework by conditioning 50% low scoring joints modes with remaining high scoring joints mode. This strategy reduces the computational cost and produces good results for 3D hand tracking on RGB-D data.