Deep Trajectory for Recognition of Human Behaviours

TL;DR

This paper introduces a novel method for human action recognition in videos by segmenting videos, extracting and representing trajectories as texture images, and training CNNs to learn motion and structural relationships, achieving superior accuracy.

Contribution

It proposes a new approach combining trajectory segmentation, texture image representation, and CNN training for improved human action recognition.

Findings

Achieves 90.01% accuracy on benchmark datasets.

Outperforms existing state-of-the-art methods.

Effectively captures structural relationships among trajectories.

Abstract

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.