As an important application of intelligent monitoring system, the action recognition in video has become a very important research area of computer vision. In order to improve the accuracy rate of the action recognition in video with improved dense trajectories, one advanced vector method is introduced. Improved dense trajectories combine Fisher Vector with Random Projection. The method realizes the reduction of the characteristic trajectory though projecting the high-dimensional trajectory descriptor into the low-dimensional subspace based on defining and analyzing Gaussian mixture model by Random Projection. And a GMM-FV hybrid model is introduced to encode the trajectory feature vector and reduce dimension. The computational complexity is reduced by Random Projection which can drop Fisher coding vector. Finally, a Linear SVM is used to classifier to predict labels. We tested the algorithm in UCF101 dataset and KTH dataset. Compared with existed some others algorithm, the result showed that the method not only reduce the computational complexity but also improved the accuracy of action recognition.
In this paper, two main algorithms about monocular visual odometry is introduced based on 3D-2D motion estimation. A 3D-2D motion estimation method needs to maintain a consistent and accurate set of triangulated 3D features and to create 3D-2D feature matches. Therefore, a keyframe selection strategy is proposed to construct the precise 3D point sets. Based on this strategy, an algorithm is designed to get more proper keyframes by restricting the number of feature points and taking translation amount into account. This keyframe selection strategy will discard inferior frames and construct more precise 3D point sets. We also designed a method to filter 3D-2D feature matches in two different ways. This method contributes to estimating camera pose more accurately. The effectiveness and feasibility of the proposed algorithms were verified in both KITTI outdoor dataset and a real indoor environment. The result of experiment showed that our algorithms can recover the motion trajectory of the camera accurately. And it meet the requirements of real-time and accuracy in monocular visual odometry.