Three-dimensional spatio-temporal trajectory descriptor for human action recognition

This paper presents a method for action recognition based on trajectory representation. The motion information for action recognition is obtained by tracking the key points through the video sequence using a standard KLT tracker. We propose a new 3D spatio-temporal descriptor based on histogram of directional derivative (3D-HODD) to describe the volume extracted around the trajectories. Our descriptor describes the local object appearance within the volume effectively and distinctively. The final descriptor constructed by combining the shape of trajectories (motion information) with 3D-HODD (appearance information). A multiclass support vector machine has been used to classify the human activities. The proposed framework for recognition of human action has been extensively validated on the benchmark datasets, with a focus that this methodology is robust and attains more precise human action recognition rate as compared to current methodologies available.