  Identifying human actions in complex scenes is widely considered as a
challenging research problem due to the unpredictable behaviors and variation
of appearances and postures. For extracting variations in motion and postures,
trajectories provide meaningful way. However, simple trajectories are normally
represented by vector of spatial coordinates. In order to identify human
actions, we must exploit structural relationship between different
trajectories. In this paper, we propose a method that divides the video into N
number of segments and then for each segment we extract trajectories. We then
compute trajectory descriptor for each segment which capture the structural
relationship among different trajectories in the video segment. For trajectory
descriptor, we project all extracted trajectories on the canvas. This will
result in texture image which can store the relative motion and structural
relationship among the trajectories. We then train Convolution Neural Network
(CNN) to capture and learn the representation from dense trajectories. .
Experimental results shows that our proposed method out performs state of the
art methods by 90.01% on benchmark data set.
