2 research outputs found
Querying 3D mesh sequences for human action retrieval
In this paper, the unsupervised human action retrieval problem in 3D
mesh sequences is addressed. An action is represented by a mesh sequence
wherein each frame is represented by a static shape descriptor. Six
state-of-the-art static descriptors are used to extract meaningful
information of the mesh objects in the sequence. These descriptors are
first examined in terms of similarity performance using Receiver
Operating Characteristic (ROC) curves. Then, they are utilized in the
action retrieval problem, where the query is a 3D mesh sequence. Each
descriptor for an action, is considered as a multidimensional curve
which traverses the corresponding points. The temporal similarity
estimation is achieved by two variations of the descriptor’s
normalization in the context of Multidimensional Dynamic Time Warping.
Experimental results are given for two standard datasets, one containing
real data and another one containing artificial data