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.

Motion Segmentation Using Optical Flow for Pedestrian Detection from Moving Vehicle

Abstract: This paper proposes a pedestrian detection method using optical flows analysis and Histogram of Oriented Gradients (HOG). Due to the time consuming problem in sliding window based, motion segmentation proposed based on optical flow analysis to localize the region of moving object. A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the ego-motion of the camera. Two consecutive images are divided into grid cells 14x14 pixels, then tracking each cell in current frame to find corresponding cells in the next frame. At least using three corresponding cells, affine transformation is performed according to each corresponding cells in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects are different from the previously registered background. Morphological process is applied to get the candidate human region. The HOG features are extracted on the candidate region and classified using linear Support Vector Machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/non-pedestrian. The proposed method was tested in a moving vehicle and shown significant improvement compare with the original HOG.

Hybrid Feature Learning and Engineering Based Approach for Face Shape Classification

Abstract: Face shape classification is a vital process to choose an appropriate eyelashes, hairstyle and facial makeup, and selection of a suitable glasses' frames according to the guidelines from experts. Measuring face characteristics by beauty experts manually costs time and efforts. Therefore, developing automated face shape identification system could alleviate the need for additional time and efforts made by experts. Many automatic face shape classification methods have been proposed in the literature; however, the existing methods tackle many challenges due to the complexity of face geometry and variation in its characteristics. This paper presents a deep convolutional neural network method (CNN) approach for classifying face shape into five types. The proposed method which is based on merging the features learnt by CNN with hand crafted features represented by histogram of oriented gradients (HOG) and facial landmarks has proven to be efficient in identification of facial shape. The obtained results demonstrate that the proposed method is promising in identifying the shape of face achieving accuracy of 81.1%.

Accelerating real-time computer vision applications using HW/SW co-design

Abstract: Video processing applications have become increasingly difficult to implement on hardware, owing to the complex computer vision algorithms involved. This paper presents a real-time video processing architecture based on hardware/software co-design that improves execution speed and reduces the time to market of applications. We have implemented this framework for handwritten digit recognition on the Zybo Zynq-7000 ARM/FPGA SoC using Vivado High Level Synthesis (HLS) and Xillybus tools. Histogram of Oriented Gradients (HOG) feature extraction algorithm has been optimised for hardware execution and acceleration techniques have been applied on Vivado HLS to achieve a speed up of 38.89 for the HOG algorithm and recognition accuracy of 95.6%. Low precision arithmetic along with our approximations for costly functions, produced this significant gain in throughput by reducing 90% of the hardware resources required with just a marginal accuracy reduction by 1%. An overall performance improvement of 77% is obtained through hardware/software co-design over software execution. The framework identified digits seamlessly in a real-time video stream at 30 frames per second and enabled high frame rate video processing.

A descriptor combining MHI and PCOG for human motion classification

Abstract: The performance of human motion classification and recognition systems is highly dependent on the distinctiveness and robustness of the feature descriptor. In this paper, a new descriptor containing motion, shape and spatial layout information is proposed, therefore it is more effective for action modeling and is suitable for detecting and recognizing a variety of actions. Experiments show that the proposed descriptor outperforms other existing methods, such as Moment Invariants and Histogram of Oriented Gradients, on recognizing human motions in an indoor environment with a stationary camera.

Hyperspectral image classification using Gradient Local Auto-Correlations

Abstract: Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.