  We consider the problem of estimating human pose and trajectory by an aerial
robot with a monocular camera in near real time. We present a preliminary
solution whose distinguishing feature is a dynamic classifier selection
architecture. In our solution, each video frame is corrected for perspective
using projective transformation. Then, two alternative feature sets are used:
(i) Histogram of Oriented Gradients (HOG) of the silhouette, (ii) Convolutional
Neural Network (CNN) features of the RGB image. The features (HOG or CNN) are
classified using a dynamic classifier. A class is defined as a pose-viewpoint
pair, and a total of 64 classes are defined to represent a forward walking and
turning gait sequence. Our solution provides three main advantages: (i)
Classification is efficient due to dynamic selection (4-class vs. 64-class
classification). (ii) Classification errors are confined to neighbors of the
true view-points. (iii) The robust temporal relationship between poses is used
to resolve the left-right ambiguities of human silhouettes. Experiments
conducted on both fronto-parallel videos and aerial videos confirm our solution
can achieve accurate pose and trajectory estimation for both scenarios. We
found using HOG features provides higher accuracy than using CNN features. For
example, applying the HOG-based variant of our scheme to the 'walking on a
figure 8-shaped path' dataset (1652 frames) achieved estimation accuracies of
99.6% for viewpoints and 96.2% for number of poses.
