Gwang-Soo Hong

Fast Coding Unit (CU) Depth Decision Algorithm for High Efficiency Video Coding (HEVC)

In this paper, we propose a fast CU depth decision algorithm for high efficiency video coding (HEVC) technology to reduce its computational complexity. In 2Nx2N prediction unit (PU), the proposed method compares to rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed-up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 43.67% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.55% is also observed without significant loss of image quality.

Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform

A convergence between a natural user interface (NUI) and advanced driver assistance system is considered as a next generation technology. This kind of interfacing system technology becomes more popular in driver assistance system of automobile. Especially, pedestrian detection is an important cue for intelligent vehicles and interactive driver assistance system. In this paper, we propose a pedestrian detection feature and technique by combining histogram of the oriented gradient (HOG) and discrete wavelet transform (DWT). In the method, the magnitude of motion is used to set region of interest (ROI) for improving detection speed. Then, we employ multi-feature for a pedestrian detection based on the HOG and DWT. In last stage, to classify whether a candidate window contains a pedestrian or not, the designed multi-feature is learned by using the training data with the support vector machine (SVM) mechanism. Experimental results show that the proposed algorithm increases the speed-up factor of 27.21 % by comparing to the existing method using the original HOG feature.

Novel Intermode Prediction Algorithm for High Efficiency Video Coding Encoder

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range images

Abstract Stereo matching is a challenging problem and highly accurate depth image is important in different applications. The main problem is to estimate the correspondence between two pixels in a stereo pair. To solve this problem, in the last decade, several cost aggregation methods aimed at improving the quality of stereo matching algorithms have been introduced. We propose a new cost aggregation method based on weighted guided image filtering (WGIF) for local stereo matching. The proposed algorithm solves multi-label problems in three steps. First, the cost volume is constructed using pixel-wise matching cost computation functions. Then, each slice of the cost volume is independently filtered using the WGIF, which substitutes for the smoothness term in the energy function. Finally, the disparity of any pixel is simply computed. The WGIF uses local weights based on a variance window of pixels in a guidance image for cost volume filtering. Experimental results using Middlebury stereo benchmark verify that the proposed method is effective due to a high quality cost volume filter.

Efficient local stereo matching technique using weighted guided image filtering (WGIF)

We propose new local stereo matching method based on weighted guided image filtering (WGIF). We utilize the WGIF by using local variance window of pixel in a guidance image which is applied to calculate local weights for cost volume filtering. The proposed method shows better performance than other cost filtering algorithms.

A ballet posture education using IT techniques: a comparative study

Recently, there were numerous ballet posture education systems using various IT techniques. We can divide these systems into three categories, such as wearable devices based systems; Kinect based systems; and other systems. Typically, these systems have a high accuracy of joint recognition. However, in certain cases, they outputs erroneous joint position. In this paper, we first provide a detailed overview of basic ballet postures and movements, discuss the state-of-the-art ballet posture education systems and then describe their main features, advantages and drawbacks.

Novel Real-Time Automobile Detection Algorithm for Blind Spot Area

Many facets of automobile technology are moving to information technology (IT)-based convergence systems. Sensors and equipment used to prevent accidents that occur due to drive negligence are under development. An automobile detection algorithm for blind spot areas while driving is proposed. Detection of candidate vehicles is based on vehicle features, and then a vehicle is subject to verification of the detected candidate vehicle area. Using a Haar-like feature for vehicle detection, an adaptive vehicle verification process based on brightness values of vehicle areas is used. The hierarchical scheme of the proposed algorithm is efficient and accurate, based on detection and verification results in the blind spot area.

Efficient Depth Map Estimation Method Based on Gradient Weight Cost Aggregation Strategy for Distributed Video Sensor Networks

Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively.

Design of Efficient Key Video Frame Protection Scheme for Multimedia Internet of Things (IoT) in Converged 5G Network

To guarantee the quality of video data into fast-responding transmission and high resolution output video using cost effective video processing is desirable in many services including Internet of Things (IoT) applications. The goal of this study is to develop a transmission method to improve a quality of service (QoS) to support for various multimedia contents with high quality on 5 generation (5G) convergence network. The main motivation is based on video feature and dependency between frames and blocks in coding structure. First, we investigate the existing methods and analyze them into some classes. From the analyzed result, we propose a priority-based key frame protection method for improving QoS of in 5G convergence network.

deepGesture: Deep learning-based gesture recognition scheme using motion sensors

Abstract Recent advancement in smart phones and sensor technology has promoted research in gesture recognition. This has made designing of efficient gesture interface easy. However, human activity recognition (HAR) through gestures is not trivial since each person may pose the same gesture differently. In this paper, we propose deepGesture algorithm, a new arm gesture recognition method based on gyroscope and accelerometer sensors using deep convolution and recurrent neural networks. This method uses four deep convolution layers to automate feature learning in raw sensor data. The features of the convolution layers are used as input of the gated recurrent unit (GRU) which is based on the state-of-the-art recurrent neural network (RNN) structure to capture long-term dependency and model sequential data. The input data of the proposed algorithm is obtained through motion sequence data extracted using a wrist-type smart band device equipped with gyroscope and accelerometer sensors. The data is initially segmented in fixed length segments. The segmented data is labeled and we construct the database. Then the labeled data is used in our learning algorithm. To verify the applicability of the algorithm, several experiments have been performed to measure the accuracy of gesture classification. Compared to the human activity recognition method, our experimental results show that the proposed deepGesture algorithm can increase the average F1-score for recognition of nine defined arm gestures by 6%.