Topic
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.
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TL;DR: A novel and efficient method for traffic sign recognition based on combination of complementary and discriminative feature sets, which has shown good complementariness and yielded fast recognition rate and is more adequate for real-time application as well.
49 citations
TL;DR: A novel railway tracks detection and turnouts recognition method using HOG (Histogram of Oriented Gradients) features was presented, which was able to correctly extract tracks and recognize turnouts even in very bad illumination conditions and run fast enough for practical use.
Abstract: Railway tracks detection and turnouts recognition are the basic tasks in driver assistance systems, which can determine the interesting regions for detecting obstacles and signals. In this paper, a novel railway tracks detection and turnouts recognition method using HOG (Histogram of Oriented Gradients) features was presented. At first, the approach computes HOG features and establishes integral images, and then extracts railway tracks by region-growing algorithm. Then based on recognizing the open direction of the turnout, we find the path where the train will travel through. Experiments demonstrated that our method was able to correctly extract tracks and recognize turnouts even in very bad illumination conditions and run fast enough for practical use. In addition, our approach only needs a computer and a cheap camera installed in the railroad vehicle, not specialized hardwares and equipment.
49 citations
TL;DR: An experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers shows that both covariance and HOG features perform very well in the context of pedestrian detection.
Abstract: Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and 'Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection.
48 citations
24 Aug 2014
TL;DR: The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15% and that histogram of oriented gradient features are able to encode similar facial features across spectrums.
Abstract: The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral face database, three HOG variants are analyzed along with dimensionality reduction approaches and linear discriminant analysis. The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15%. We also observe that histogram of oriented gradient features are able to encode similar facial features across spectrums.
48 citations
02 Sep 2009
TL;DR: A new HoG (Histogram of Oriented Gradients) tracker for Gesture Recognition is introduced to build HoG trajectory descriptors (representing local motion) which are used for gesture recognition.
Abstract: We introduce a new HoG (Histogram of Oriented Gradients) tracker for Gesture Recognition. Our main contribution is to build HoG trajectory descriptors (representing local motion) which are used for gesture recognition. First,we select for each individual in the scene a set of corner points to determine textured regions where to compute 2DHoG descriptors. Second, we track these 2D HoG descriptors in order to build temporal HoG descriptors. Lost descriptors are replaced by newly detected ones. Finally, we extract the local motion descriptors to learn offline a set of given gestures.Then, a new video can be classified according to the gesture occurring in the video. Results shows that the tracker performs well compared to KLT tracker [1]. The generated local motion descriptors are validated through gesture learning-classification using the KTH action database [2].
48 citations