• Journal of Information Processing Systems
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• 제8권3호
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.120-124
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• 2021

Recently, human attribute recognition (HAR) attracts a lot of attention due to its wide application in video surveillance systems. Recent deep-learning-based solutions for HAR require time-consuming training processes. In this paper, we propose a post-processing technique that utilizes the historical video frames to improve prediction results without invoking re-training or modifying existing deep-learning-based classifiers. Experiment results on a large-scale benchmark dataset show the effectiveness of our proposed method.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.288-292
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• 2016

In this paper, we propose an infrared image based human victim recognition method for a search and rescue robot in dark environments, like general disaster situations. For recognizing a human victim, an infrared camera on a RGB-D camera, Microsoft Kinect, is used. The contrast and brightness of the infrared image are first improved by histogram equalization, and the noise on the image is removed by morphological operation and Gaussian filtering. For recognizing a human victim, the binarization and blob labeling methods are applied to the improved image. Finally, for verifying the effectiveness and feasibility of the proposed method, an experiment for human victim recognition is carried out in a dark environment.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제1권1호
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• pp.1-8
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• 2020

Recent studies revealed that many animals identify individual humans. In this account, we review previous literatures on individual human recognition by wild or domestic animals and discuss the three hypotheses: "high cognitive abilities" hypothesis, "close human contact" and "pre-exposure to stimuli" hypothesis. The three hypotheses are not mutually exclusive. Close human contact hypothesis is an ultimate explanation for adaptive benefits whereas high cognitive abilities and pre-exposure to stimuli hypothesis are proximate explanations for mechanisms to perform such discriminatory behaviour. We report a case study of two bird species in a human-free habitat. Long-tailed skuas, which are known for having high cognitive abilities, exhibited the human discriminatory abilities whereas ruddy turnstones did not display such abilities toward approaching humans. This suggests that highly intelligent species may have this type of discriminatory ability so that they could learn to identify individual humans quickly by pre-exposure to stimuli, even in a human-free habitat. Here, we discuss that human recognition is more common in species with rapid learning ability and it could develop for a short period of time between an intelligent species and human.

• 한국멀티미디어학회논문지
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• 제17권5호
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• pp.556-565
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• 2014

This paper proposes a method to track object of real-time video transferred through single web-camera and to recognize risk-situation and human actions. The proposed method recognizes human basic actions that human can do in daily life and finds risk-situation such as faint and falling down to classify usual action and risk-situation. The proposed method models the background, obtains the difference image between input image and the modeled background image, extracts human object from input image, tracts object's motion and recognizes human actions. Tracking object uses the moment information of extracting object and the characteristic of object's recognition is moment's change and ratio of object's size between frames. Actions classified are four actions of walking, waling diagonally, sitting down, standing up among the most actions human do in daily life and suddenly falling down is classified into risk-situation. To test the proposed method, we applied it for eight participants from a video of a web-cam, classify human action and recognize risk-situation. The test result showed more than 97 percent recognition rate for each action and 100 percent recognition rate for risk-situation by the proposed method.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.977-985
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• 2020

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.

• 정보과학회 논문지
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• 제42권1호
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• pp.138-143
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• 2015

본 논문에서는 시각장애인의 장면인식 보조를 위해, 귀걸이 형 블루투수 카메라와 행동인식 서버간의 통신을 통해 휴먼의 행동을 인식하는 시스템을 제안한다. 먼저 시각장애인이 귀걸이 형 블루투수 카메라를 이용하여 원하는 위치의 장면을 촬영하면, 촬영된 영상은 카메라와 연동된 스마트 폰을 통해 인식서버로 전송된다. 인식 서버에서는 영상 분석 알고리즘을 이용하여 휴먼 및 객체를 검출하고 휴먼의 포즈를 분석하여 휴먼 행동을 인식한다. 인식된 휴먼 행동 정보는 스마트 폰에 재 전송되고 사용자는 스마트 폰을 통해 text-to-speech (TTS)로 인식결과를 듣게 된다. 본 논문에서 제안한 시스템에서는 실내 외에서 촬영된 실험데이터에 대해서 60.7%의 휴먼 행동 인식 성능을 보여 주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.