Action Recognition with HOG-OF Features
نویسنده
چکیده
In this paper a simple and efficient framework for single human action recognition is proposed. In two parallel processing streams, motion information and static object appearances are gathered by introducing a frame-by-frame learning approach. For each processing stream a Random Forest classifier is separately learned. The final decision is determined by combining both probability functions. The proposed recognition system is evaluated on the KTH data set for single human action recognition with original training/testing splits and a 5-fold cross validation. The results demonstrate state-of-the-art accuracies with an overall training time of 30 seconds on a standard workstation.
منابع مشابه
Action Change Detection in Video Based on HOG
Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one ...
متن کاملBottom-up Attention Improves Action Recognition Using Histograms of Oriented Gradients
When recognizing others’ action, we pay attention to their body parts and/or objects they are manipulating rather than observing their whole body movement. Bottom-up saliency is a promising cue to determine where to attend and hence to identify what the persons are doing because their body parts acting on objects become more conspicuous when contributing to the action. This paper proposes an ar...
متن کاملContinuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model
In this paper, we propose a new method for spotting and recognizing continuous human actions using a vision sensor. The method is comprised of depth-MHI-HOG (DMH), action modeling, action spotting, and recognition. First, to effectively separate the foreground from background, we propose a method called DMH. It includes a standard structure for segmenting images and extracting features by using...
متن کاملHOG Features on the GPU
Histogram of Oriented Gradients (HOG) features are a trending topic in object detection literature. HOG features are a robust way of describing local object appearances and shapes by their distribution of intensity gradients or edge directions, and have been used successfully as a low level feature in a number of object recognition tasks. Human faces are generally considered interesting and imp...
متن کاملMultilingual scene character recognition with co-occurrence of histogram of oriented gradients
Automatic machine reading of texts in scenes is largely restricted by the poor character recognition accuracy. In this paper, we extend the Histogram of Oriented Gradient (HOG) and propose two new feature descriptors: Cooccurrence HOG (Co-HOG) and Convolutional Co-HOG (ConvCo-HOG) for accurate recognition of scene texts of different languages. Compared with HOG which counts orientation frequenc...
متن کاملWorking papers in transport, tourism, information technology and microdata analysis Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs
The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities. HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked ...
متن کامل