• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.9
• /
• pp.139-147
• /
• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.5
• /
• pp.175-182
• /
• 2010

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

• Journal of Broadcast Engineering
• /
• v.17 no.6
• /
• pp.1091-1105
• /
• 2012

In this paper, we analyzed statistical characteristics and complexity of HEVC encoder as a leading research of acceleration, optimization and parallelization. Computational complexity of the HEVC encoder is approximately twice the compression performance compared to H.264/AVC. But, the increase of encoder complexity remains a problem to be solved in the future. Before performing the research on acceleration, optimization and parallelization to reduce high complexity of HEVC encoder, we measure the complexity each module for HEVC encoder using it's reference software HM 7.1. We also measured the predicted complexity of fast HEVC encoder software, used in real applications, using HM 7.1 applying fast encoding method. The complexity is measured in terms of the operating cycle of the encoder software under the common test sequences and conditions in the Windows PC environment. In addition, we analyze statistical characteristics of HEVC encoder software according to encoding structures and limitation using coded bitstreams.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.22-29
• /
• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2171-2178
• /
• 1999

In these days, as rapid growth of multimedia industries and development of techniques, and effort to develop DAVIC(Digital Audio-Visual Council) systems which support multimedia services such as VOD(Video onn Demand) and teleshopping is being done. Therefore it will be indispendable to establish a new conformance testing method related DAVIC system with respect to their standard specification. DSM-CC is a core part of DAVIC and adopts DSM-CC U-N for S3 information stream which plays a part in connection establishment and release of session and transmission layer, and DSM-CC U-U for S2 which operates user application of the system. In this paper, we propose a new conformance testing architecture and methodology based on scenario in order to test DSM-CC U-U which doesn't have any message sequences.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.2C
• /
• pp.193-202
• /
• 2010

In this paper, we propose a fast algorithm that can reduce the complexity for inter mode decision of the H.264 encoder. The main idea consists of two techniques. The first one is the technique early terminating mode decision process. We focused on the skip and $16{\times}16$ mode because these modes occupies the largest portion in most of sequences. The second one is the technique skipping unnecessary $8{\times}8$ modes. The time consumption caused by the $8{\times}8$ mode is very considerable. Therefore if we can extract the unnecessary $8{\times}8$ mode calculation well, a large amount of time can be saved in total encoding process. The experimental results show that the proposed algorithm can achieve up to 43% speed up ratio with insignificant PSNR loss. The increase of total bits encoded is also not noticeable.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.4
• /
• pp.250-257
• /
• 2011

In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.2
• /
• pp.7-14
• /
• 2002

This paper proposes a camera motion estimation technique using special relations of motion vectors of geometrically symmetrical triple points of two consecutive views of single camera. The proposed technique uses camera-induced motion vectors and their relations other than feature points and epioplar constraints. As contrast to the time consuming iterations or numerical methods in the calculation of E-matrix or F-matrix induced by epipolar constraints, the proposed technique calculates camera motion parameters such as panning, tilting, rolling, and zooming at once by applying the proposed linear equation sets to the motion vectors. And by devised background discriminants, it effectively reflects only the background region into the calculation of motion parameters, thus making the calculation more accurate and fast enough to accommodate MPEG-4 requirements. Experimental results on various types of sequences show the validity and the broad applicability of the proposed technique.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.2
• /
• pp.20-28
• /
• 2003

Recently we need a faster and more accurate motion vector search algorithm for ASIC(Application Specific IC) or small systems. Block motion estimation using Full Search(FS) algorithm provides the best visual quality and PSNR, but it requires intensive computations. The previously proposed fast algorithms reduced the number of computations by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher SAD(Sum of Absolute Difference) for motion compensated images. In this paper we exploit the spatial correlation of motion vectors and present a fast motion estimation scheme which uses the predicted motion vector(PMV). The PMV scheme is more clear and simpler than the previously proposed algorithms which also use adjacent motion vectors. Simulation results with standard video sequences show that the PMV scheme is faster and more accurate than other algorithms such as Nearest-Neighbors Search(NNS) algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.5
• /
• pp.73-83
• /
• 2007

In this paper, we propose macroblock(MB)-based adaptive interpolation filter method for improving coding efficiency in H.264/AVC. In the proposed method, nine separable two-dimensional(2D) interpolation filters are applied for precisely compensating motions in various directions. The optimal cost function which considers the bit rate and distortion for coding the MB is defined. The filter is adaptively selected per MB for minimizing the defined cost function. In the experimental results, the proposed method shows more excellent in coding efficiency than the conventional methods for the various standard $QCIF(176{\times}144)/CIF(352{\times}288)$ video test sequences. It leads to about 6.25%(1 reference frame) and 3.46%(5 reference frames) bit rate reduction on average compared to the H.264/AVC.