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.

Superpixels shape analysis for carried object detection

Abstract: Video surveillance systems generate enormous amounts of data which makes the continuous monitoring of video a very difficult task Re-identification of subjects in video surveillance systems plays a significant role in public safety Recent work has focused on appearance modeling and dis- tance learning to establish correspondence between images However, real-life scenarios suggest that the majority of clothing worn tends to be non-discriminative Attributes- based re-identification methods try to solve this problem by incorporating semantic attributes which are mid-level fea- tures learned a prior In this paper we present a framework to recognize attributes with applications to carried objects detection We present a supervised approach based on the contours and shapes of superpixels and histogram of oriented gradients An experimental evaluation is described using a dataset that was recorded at the Greater Cleveland Regional Transit Authority

Real-time people detection and mapping system for a mobile robot using a RGB-D sensor

Abstract: In this paper, we present a robotic system capable of mapping indoor, cluttered environments and, simultaneously, detecting people and localizing them with respect to the map, in real-time, using solely a Red-Green-Blue and Depth (RGB-D) sensor, the Microsoft Kinect, mounted on top of a mobile robotic platform running Robot Operating System (ROS). The system projects depth measures in a plane for mapping purposes, using a grid-based Simultaneous Localization and Mapping (SLAM) approach, and pre-processes the sensor's point cloud to lower the computational load of people detection, which is performed using a classical technique based on Histogram of Oriented Gradients (HOG) features, and a linear Support Vector Machine (SVM) classifier. Results show the effectiveness of the approach and the potential to use the Kinect in real world scenarios.

A Robust Multi-Model Approach for Face Detection in Crowd

Abstract: The estimation of the number of people in surveillance areas is essential for monitoring crowded scenes. When density of a zone increases to a certain approximated level, people's safety can be endangered. Detection of human is a prerequisite for density estimation, tracking, activity recognition and anomaly detection even in non congested areas. This paper presents a robust hybrid approach for face detection in crowd by combining the skin color segmentation and a Histogram of Oriented Gradients(HOG) with Support Vector Machine(SVM) architecture. Initially, image enhancement is performed to improve the detection rate. An edge preserving pyramidal approach is applied for multiscale representation of an image. Skin color segmentation is done with combination of YCbCr and RGB color model, and HOG features are extracted from the segmented skin region. We trained the SVM classifier by Muct and FEI databases which consist 751 and 2800 face images respectively. The accuracy of this approach is evaluated by testing it on BAO multiple face database and on various manually collected images captured in surveillance areas. Experimental results demonstrate that the supplementary skin color segmentation with HOG is more potent for increasing the detection rate than using HOG features only. The proposed approach achieves 98.02% accuracy which is higher in comparison to Viola Jones and fast face detection method.

Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking.

Abstract: Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to be a challenging problem due to the unpredictability of various real-time situations, such as illumination variations, occlusion, fast motion, deformation, and scale variation, even though we only know the initial target position. To address these matters, we used a kernelized-correlation-filter-based translation filter with the integration of multiple features such as histogram of oriented gradients (HOG) and color attributes. These powerful features are useful to differentiate the target from the surrounding background and are effective for motion blur and illumination variations. To minimize the scale variation problem, we designed a correlation-filter-based scale filter. The proposed adaptive model’s updating and dynamic learning rate strategies based on a peak-to-sidelobe ratio effectively reduce model-drifting problems by avoiding noisy appearance changes. The experiment results show that our method provides the best performance compared to other methods, with a distance precision score of 79.9%, overlap success score of 59.0%, and an average running speed of 74 frames per second on the object tracking benchmark (OTB-2015).

On Multifont Character Classification in Telugu

Abstract: A major requirement in the design of robust OCRs is the invariance of feature extraction scheme with the popular fonts used in the print. Many statistical and structural features have been tried for character classification in the past. In this paper, we get motivated by the recent successes in object category recognition literature and use a spatial extension of the histogram of oriented gradients (HOG) for character classification. Our experiments are conducted on 1453950 Telugu character samples in 359 classes and 15 fonts. On this data set, we obtain an accuracy of 96-98% with an SVM classifier.