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Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.


Papers
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Journal ArticleDOI
TL;DR: A computer vision based algorithm for recognizing single actions of earthmoving construction equipment, based on a multiple binary SVM classifier and spatio-temporal features, which outperforms previous algorithms for excavator and truck action recognition.

215 citations

Proceedings ArticleDOI
21 Mar 2011
TL;DR: A method for automatic emotion recognition that uses support vector machine (SVM) and largest margin nearest neighbour (LMNN) and compares the results to the pre-computed FERA 2011 emotion challenge baseline.
Abstract: We propose a method for automatic emotion recognition as part of the FERA 2011 competition. The system extracts pyramid of histogram of gradients (PHOG) and local phase quantisation (LPQ) features for encoding the shape and appearance information. For selecting the key frames, K-means clustering is applied to the normalised shape vectors derived from constraint local model (CLM) based face tracking on the image sequences. Shape vectors closest to the cluster centers are then used to extract the shape and appearance features. We demonstrate the results on the SSPNET GEMEP-FERA dataset. It comprises of both person specific and person independent partitions. For emotion classification we use support vector machine (SVM) and largest margin nearest neighbour (LMNN) and compare our results to the pre-computed FERA 2011 emotion challenge baseline.

214 citations

Journal ArticleDOI
TL;DR: The simulation results show the deep feature plus SVM perform better classification compared to transfer learning counterpart, and the F1 score of CNN classification models was compared with other traditional image classification models.

208 citations

Journal ArticleDOI
TL;DR: The approach for classifying acoustic scenes is based on transforming the audio signal into a time-frequency representation and then in extracting relevant features about shapes and evolutions of time- frequency structures based on histogram of gradients that are subsequently fed to a multi-class linear support vector machines.
Abstract: This abstract presents our entry to the Detection and Classification of Acoustic Scenes challenge. The approach we propose for classifying acoustic scenes is based on transforming the audio signal into a time–frequency representation and then in extracting relevant features about shapes and evolutions of time–frequency structures. These features are based on histogram of gradients that are subsequently fed to a multi-class linear support vector machines.

206 citations

Journal ArticleDOI
TL;DR: The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively, and demonstrates its suitability for automatic initialization of vision trackers.

204 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022181
2021116
2020189
2019179
2018240