Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor
نویسندگان
چکیده
In this paper we apply the Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) descriptor to the field of human action recognition. A video sequence is described as a collection of spatial-temporal words after the detection of space-time interest points and the description of the area around them. Our contribution has been in the description part, showing LBP-TOP to be a promising descriptor for human action classification purposes. We have also developed several extensions to the descriptor to enhance its performance in human action recognition, showing the method to be computationally efficient.
منابع مشابه
Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition
Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datas...
متن کاملHuman Action Recognition Based on 3D Edge Oriented Gradient Histogram of Slide Blocks
In this paper, a new feature called 3D edge oriented gradient histogram of slide blocks is proposed for human action recognition, based on the idea that the slide area of human body edge can be seen as a spatio-temporal silhouette surface when human performing a certain action in video. This feature is processed by defining dense 3D spatio-temporal slide blocks on the spatio-temporal silhouette...
متن کاملGenetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition
The potential value of human action recognition has led to it becoming one of the most active research subjects in computer vision. In this paper, we propose a novel method to automatically generate low-level spatio-temporal descriptors showing good performance, for high-level human-action recognition tasks. We address this as an optimization problem using genetic programming (GP), an evolution...
متن کاملAdaptive Tuboid Shapes for Action Recognition
Encoding local motion information using spatio-temporal features is a common approach in action recognition methods. These features are based on the information content inside subregions extracted at locations of interest in a video. In this paper, we propose a conceptually different approach to video feature extraction. We adopt an entropybased saliency framework and develop a method for estim...
متن کاملRecognition of Visual Events using Spatio-Temporal Information of the Video Signal
Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approac...
متن کامل