1. Eigen-Evolution Dense Trajectory Descriptors
Good afternoon. My name is Minh Hoai, and I am from Stony Brook University.
This is the joint work with my students Yang Wang and Vinh Tran.
Yang Wang, Vinh Tran, and Minh Hoai
Stony Brook University
FG2018 Poster Session #: 2
Poster ID #: 10

2. Eigen-Evolution Dense Trajectory Descriptors
We propose new descriptors for human action recognition
Main idea: encode the temporal evolution of features
Feature vectors
Ordered set of 1D functions
Decompose each function as a linear combination of basis functions
We propose a new type of descriptors for human action recognition
The main idea is to encode the temporal evolution of features
Given a sequence of feature vectors such as a sequence of CNN-based feature vectors
We propose to view the sequence of features as an ordered set of 1D functions instead
Each function tracks the change of a feature over time
We decompose each function as a linear combination of some basis functions.
The coefficients can be grouped together to define a new representation for the sequence of input feature vectors.
This descriptor encodes the temporal evolution of the features over time.
Proposed descriptors

3. Eigen-Evolution Dense Trajectory Descriptors
We obtain a method for encoding a sequence of feature vectors
It is applicable to different kind of feature sequences
We apply to Dense Trajectory Descriptors
We obtain the state-of-the-art action recognition result on Hollywood 2 dataset: 78.7%
We obtain a method for encoding a sequence of feature vectors.
We apply it to encode dense trajectory descriptors, and obtain state of the art results on Hollywood 2 dataset.
Please stop by our poster to learn more about it.