• ETRI Journal
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• v.42 no.1
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• pp.78-89
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• 2020

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5006-5022
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• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.8
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• pp.867-874
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• 2015

Hoist have been installed at wharfs or seashore in the center of fishing village fraternities. A Hoist is used in harbor loading and unloading fishing gear or seafoods and is a device to refloat fishing boats into a breakwater or land in case of typhoon or bad weather. In this paper, we propose image perception and moving objects tracking system that detects boat's damage, theft and trespassing occurred at the wharf. This system detects objects' motion in real time by using the motion templet and tracks to concentrate on a moving object(person, boat, etc.) by using a PTZ camera.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.955-959
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• 2009

In this paper, we propose a novel method for analyzing human interactions based on the walking trajectories of human subjects. Our principal assumption is that an interaction episode is composed of meaningful smaller unit interactions, which we call 'sub-interactions.' The whole interactions are represented by an ordered concatenation or a network of sub-interaction models. From the experiments, we could confirm the effectiveness and robustness of the proposed method by analyzing the inner workings of an interaction network and comparing the performance with other previous approaches.

• Journal of Internet of Things and Convergence
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• v.3 no.2
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• pp.1-5
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• 2017

As CCTV technology develops, it is used in various fields. Currently, we want to know about CCTV operation in detail. In addition, CCTV in many fields is causing problems in operation. We plan to design a new system to solve the problem. In this paper, we analyze data using K-means so that CCTV can be operated efficiently, add new technology and function to existing system to increase image technology and operate efficiently, Technology. In addition, we will design a new system for CCTV technology using k-means so that the CCTV can be efficiently operated in the center, and propose the problem to solve the problem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.472-477
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• 2016

The most important and critical to the performance of video surveillance systems is to be detected exactly how much. In order to accurately track the object must be able to accurately separate the background and object. However, the system itself rather than the human vision exactly distinguish the object and the background, to assess the situation, it is not easy. If we can accurately detect the background and the object, to be able to accurately track an object, it is possible to increase the reliability of the system, have a significant impact on the success of the entire production system. In this paper, we propose a way to distinguish more precisely the background and the object being to determine the background environment changes more accurately.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1261-1271
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• 2013

Zoom tracking involves the automatic adjustment of the focus motor in response to the zoom motor movements for the purpose of keeping an object of interest in focus, and is typically achieved by moving the zoom and focus motors in a zoom lens module so as to follow the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. Thus, one can simply implement zoom tracking by following the most closest trace curve after all the trace curve data are stored in memory. However, this approach is often prohibitive in practical implementation because of its large memory requirement. Many other zoom tracking methods such as GZT, AZT and etc. have been proposed to avoid large memory requirement but with a deteriorated performance. In this paper, we propose a new zoom tracking method called 'Approximate Feedback Zoom Tracking method (AFZT)' on DM36x-based IP network camera, which does not need large memory by approximating nearby trace curves, but generates better zoom tracking accuracy than GZT or AZT by utilizing focus value as feedback information. Experiments through real implementation shows the proposed zoom tracking method improves the tracking performance and works in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2075-2092
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• 2017

Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.

• Journal of Gastric Cancer
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• v.14 no.3
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• pp.207-210
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• 2014

The peritoneum is the most frequent site of recurrence for gastric cancer after gastrectomy, followed by the liver and lymph nodes. In contrast, metastasis to the thymus is rare. Annual surveillance with computed tomography was performed on a 67-year-old man who previously underwent a distal gastrectomy and D2 lymph node dissection for gastric cancer at Tottori University. Five years after the initial operation, an anterior mediastinal tumor was detected by computed tomography. The patient underwent video-assisted thoracic surgery to remove the tumor. Histopathology revealed adenocarcinoma cells similar to those of the gastric cancer resected 5 years previously. Thymic metastasis was considered likely based on the location of the tumor. The recognition that gastric cancer can metastasize to unusual anatomic locations, such as the thymus, can facilitate an accurate, prompt diagnosis and appropriate treatment.

• Journal of Industrial Technology
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• v.38 no.1
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• pp.41-52
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.