• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.67-73
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• 2020

In this paper, we present a CNN-based gesture recognition approach which reduces the memory burden of input data. Most of the neural network-based gesture recognition methods have used a sequence of frame images as input data, which cause a memory burden problem. We use a motion history image in order to define a meaningful gesture. The motion history image is a grayscale image into which the temporal motion information is collapsed by synthesizing silhouette images of a user during the period of one meaningful gesture. In this paper, we first summarize the previous traditional approaches and neural network-based approaches for gesture recognition. Then we explain the data preprocessing procedure for making the motion history image and the neural network architecture with three convolution layers for recognizing the meaningful gestures. In the experiments, we trained five types of gestures, namely those for charging power, shooting left, shooting right, kicking left, and kicking right. The accuracy of gesture recognition was measured by adjusting the number of filters in each layer in the proposed network. We use a grayscale image with 240 × 320 resolution which defines one meaningful gesture and achieved a gesture recognition accuracy of 98.24%.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.429-437
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• 2017

2D-to-3D conversion technology has been studied over past decades and integrated to commercial 3D displays and 3DTVs. Generally, depth cues extracted from a static image is used for generating a depth map followed by DIBR (Depth Image Based Rendering) for producing a stereoscopic image. Further, motion is also an important cue for depth estimation and is estimated by block-based motion estimation, optical flow and so forth. This papers proposes a new method for motion depth generation using Motion History Image (MHI) and evaluates the feasiblity of the MHI utilization. In the experiments, the proposed method was performed on eight video clips with a variety of motion classes. From a qualitative test on motion depth maps as well as the comparison of the processing time, we validated the feasibility of the proposed method.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.39-42
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• 2012

This paper proposed a method that detects emergency situations in a video stream using MHI (Motion History Image) and template matching for a video-based intelligent security system. The proposed method creates a MHI of each human object through image processing technique such as background removing based on GMM (Gaussian Mixture Model), labeling and accumulating the foreground images, then the obtained MHI is compared with the existing MHI templates for detecting an emergency situation. To evaluate the proposed emergency detection method, a set of experiments on the dataset of video clips captured from a security camera has been conducted. And we successfully detected emergency situations using the proposed method. In addition, the implemented system also provides MMS (Multimedia Message Service) so that a security manager can deal with the emergency situation appropriately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2733-2737
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• 2014

Motion detection according to the movement and the change area detection method according to the background difference and the motion history image for use in a motion estimation technique using a real-time image, the motion detection method according to the optical flow, the back-projection of the histogram of the object to track for motion tracking At the heart of MeanShift center point of the object and the object to track, while used, the size, and the like due to the motion tracking algorithm CamShift, Kalman filter to track with direction. In this paper, we implemented the motion detection algorithm based on color and shape information of the object and verify.

• Annual Conference of KIPS
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• 2014.04a
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• pp.901-903
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• 2014

논문에서는 비디오 감시 시스템을 기반으로 축사 내 환경 및 상황을 모니터링하고 최적의 번식 적기를 판별하기 위한 시스템을 제안한다. 본 논문에서 제안된 시스템은 영상 데이터로부터 각 프레임의 Motion History Image 처리를 이용하여 움직임 벡터를 추출하고 이를 유효한 움직임 벡터로 분류한다. 움직임 벡터의 크기와 방향이 임계값보다 큰 경우 해당 장면을 특정 상황으로 분류한다. 실제 촬영한 영상 데이터를 통해 실험한 결과, 승가 상황에서 확연한 결과값의 차이가 있었고, 이를 이용하여 한우의 승가 상황 검출이 가능함을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.1-13
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• 2011

In this paper, we propose a gesture recognition system to recognize motions using the shape information of MHI (Motion History Image). The system acquires MHI to provide information on motions from images with input and extracts the gradient images from such MHI for each X and Y coordinate. It extracts the shape information by applying the shape context to each gradient image and uses the extracted pattern information values as the feature values. It recognizes motions by learning and classifying the obtained feature values with a SVM (Support Vector Machine) classifier. The suggested system is able to recognize the motions for multiple people as well as to recognize the direction of movements by using the shape information of MHI. In addition, it shows a high ratio of recognition with a simple method to extract features.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.47-57
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• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.9-14
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• 2014

Recently, various contents using human motion are developed in computer vision and game industries. If we try to apply human motion to application programs and contents, users can experience a sense of immersion getting into it so that the users feel a high level of satisfaction from the contents. In this research, we analyze human head motion using images captured from an webcam and then we apply the result of motion recognition to a game without special devices as an interface. The proposed method, first, segments human head region using an image composed of MHI(Motion History Image) and the result of skin color detection, and then we calculate the direction and distance by the MHI sequence. In experiments, the proposed method for human head motion recognition was tested for controlling a game player. From the experimental results we proved that the proposed method can make a gammer feel more immersed into the game. Furthermore, we expect the proposed method can be an interface of a serious game for medical or rehabilitation purposes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1731-1737
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• 2016

In this paper, a method is proposed to recognize human actions as nonverbal expression; the proposed method is composed of two steps which are action representation and action recognition. First, MHI(Motion History Image) is used in the action representation step. This method includes segmentation based on depth information and generates spatio-temporal templates to describe actions. Second, CNN(Convolution Neural Network) which includes feature extraction and classification is employed in the action recognition step. It extracts convolution feature vectors and then uses a classifier to recognize actions. The recognition performance of the proposed method is demonstrated by comparing other action recognition methods in experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.537-542
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• 2011

A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.