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.

Fast HOG based person detection devoted to a mobile robot with a spherical camera

Abstract: In this paper, we present a fast Histogram of Oriented Gradients (HOG) based person detector. The detector adopts a cascade of rejectors framework by selecting discriminant features via a new proposed feature selection framework based on Binary Integer Programming. The mathematical programming explicitly formulates an optimization problem to select discriminant features taking detection performance and computation time into account. The learning of the cascade classifier and its detection capability are validated using a proprietary dataset acquired using the Ladybug2 spherical camera and the public INRIA person detection dataset. The final detector achieves a comparable detection performance as Dalal and Triggs [2] detector while achieving on average more than 2.5×-8× speed up depending on the training dataset.

Image analysis and computer vision: 1990

Abstract: This paper presents a bibliography of over 1600 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection, segmentation, and image analysis; matching, stereo, and time-varying imagery; shape and pattern; color and texture; and three-dimensional scene analysis. A few references are also given on related topics, such as computational geometry, computer graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition, and artificial intelligence.

A Real-time Scalable Object Detection System using Low-power HOG Accelerator VLSI

Abstract: As described in this paper, a real-time object detection system using a Histogram of Oriented Gradients (HOG) feature extraction accelerator VLSI is presented. The VLSI [1, 2] enables the system to achieve real-time performance and scalability for multiple object detection under limited power condition. The VLSI employs three techniques: a VLSI-oriented HOG algorithm with early classification in Support Vector Machine (SVM) classification, a dual-core architecture for parallel feature extraction, and a detection-window-size scalable architecture with a reconfigurable MAC array for processing objects of different shapes. The test chip was fabricated using 65 nm CMOS technology. The measurement result shows that the VLSI consumes 43 mW at 42.9 MHz and 1.1 V to process HDTV (1920?×?1080 pixels) at 30 frames per second (fps). A multiple object detection system and a multiple scale object detection system are presented to demonstrate the system flexibility and scalability realized by VLSI and applicability for versatile application of object detection. On the multiple object detection system, a real-time object detection for HDTV resolution video is achieved with 84 mW of power consumption on a task to detect 2 types of targets while keeping comparable detection accuracy as software-based system. On the multiple scale object detection system, a task to detect 5 scales of a target is accomplished using a single VLSI. The power consumption of the VLSI is estimated to 102 mW on the task.

Facial expression recognition with emotion-based feature fusion

Abstract: In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors for facial expression representation, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), and Pyramid of Histogram of Oriented Gradients (PHOG), are considered. Supervised Locality Preserving Projection (SLPP) is applied to the respective features for dimensionality reduction and manifold learning. Experiments show that descriptors are also sensitive to the conditions of images, such as race, lighting, pose, etc. Thus, an adaptive descriptor selection algorithm is proposed, which determines the best two features for each expression class on a given training set. These two features are fused, so as to achieve a higher recognition rate for each expression. In our experiments, the JAFFE and BAUM-2 databases are used, and experiment results show that the descriptor selection step increases the recognition rate up to 2%.

Deep learning of structured environments for robot search

Abstract: Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a deep learning based approach to predict exit locations of buildings. This technique exploits the inherent structure of buildings to create a model. A convolutional neural network is trained using a database of building blueprints and used to guide a search within a building. This technique is compared to standard frontier exploration and a traditional image processing approach of extracting features through histogram of gradients (HOG) and training a support vector machine (SVM). After validation through simulation, we show that the proposed deep learning technique reduces the amount of building exploration required to find the goal by 36%.