1 research outputs found
Dynamic Template Tracking and Recognition
In this paper we address the problem of tracking non-rigid objects whose
local appearance and motion changes as a function of time. This class of
objects includes dynamic textures such as steam, fire, smoke, water, etc., as
well as articulated objects such as humans performing various actions. We model
the temporal evolution of the object's appearance/motion using a Linear
Dynamical System (LDS). We learn such models from sample videos and use them as
dynamic templates for tracking objects in novel videos. We pose the problem of
tracking a dynamic non-rigid object in the current frame as a maximum
a-posteriori estimate of the location of the object and the latent state of the
dynamical system, given the current image features and the best estimate of the
state in the previous frame. The advantage of our approach is that we can
specify a-priori the type of texture to be tracked in the scene by using
previously trained models for the dynamics of these textures. Our framework
naturally generalizes common tracking methods such as SSD and kernel-based
tracking from static templates to dynamic templates. We test our algorithm on
synthetic as well as real examples of dynamic textures and show that our simple
dynamics-based trackers perform at par if not better than the state-of-the-art.
Since our approach is general and applicable to any image feature, we also
apply it to the problem of human action tracking and build action-specific
optical flow trackers that perform better than the state-of-the-art when
tracking a human performing a particular action. Finally, since our approach is
generative, we can use a-priori trained trackers for different texture or
action classes to simultaneously track and recognize the texture or action in
the video