  Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused
on acquiring behavioral information of the targets and their activities.
Continuous evolution of intelligence being gathered of the human centric
activities has put increased focus on the humans, especially inferring their
innate characteristics - size, shapes and physiology. These bio-signatures
extracted from the surveillance sensors can be used to deduce age, ethnicity,
gender and actions, and further characterize human actions in unseen scenarios.
However, recovery of pose and shape of humans in such monocular videos is
inherently an ill-posed problem, marked by frequent depth and view based
ambiguities due to self-occlusion, foreshortening and misalignment. The
likelihood function often yields a highly multimodal posterior that is
difficult to propagate even using the most advanced particle filtering(PF)
algorithms. Motivated by the recent success of the discriminative approaches to
efficiently predict 3D poses directly from the 2D images, we present several
principled approaches to integrate predictive cues using learned regression
models to sustain multimodality of the posterior during tracking. Additionally,
these learned priors can be actively adapted to the test data using a
likelihood based feedback mechanism. Estimated 3D poses are then used to fit 3D
human shape model to each frame independently for inferring anthropometric
bio-signatures. The proposed system is fully automated, robust to noisy test
data and has ability to swiftly recover from tracking failures even after
confronting with significant errors. We evaluate the system on a large number
of monocular human motion sequences.
