• Journal of Korea Multimedia Society
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• v.17 no.5
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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.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.420-432
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• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.9 s.351
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• pp.44-52
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• 2006

Recently developed MPEG-4 Part 2 compression standard provides a novel capability to handle arbitrary video objects. To support this capability, an efficient object segmentation technique is required. This paper proposes a real-time algorithm for foreground object segmentation in video sequences. The proposed algorithm consists of two steps: the first step that segments a video frame into multiple sub-regions using Spatio-Temporal Watershed Transform and the second step in which a foreground object segment is extracted from the sub-regions generated in the first step. For real-time processing, the algorithm is partitioned into hardware and software parts so that computationally expensive parts are off-loaded from a processor and executed by hardware accelerators. Simulation results show that the proposed implementation can handle QCIF-size video at 15 fps and extracts an accurate foreground object.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.43-50
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• 2009

This paper caters the need of acquiring the principal objects, characters, and scenes from a video in order to entertain the image based query. The movie frames are divided into frames with 2D representative images called "key frames". Various regions in a key frame are marked as key objects according to their textures and shapes. These key objects serve as a catalogue of regions to be searched and matched from rest of the movie, using viewpoint invariant regions calculation, providing the location, size, and orientation of all the objects occurring in the movie in the form of a set of structures collaborating as video profile. The profile provides information about occurrences of every single key object from every frame of the movie it exists in. This information can further ease streaming of objects over various network-based viewing qualities. Hence, the method provides an effective reduced profiling approach of automatic logging and viewing information through query by example (QBE) procedure, and deals with video streaming issues at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2444-2463
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• 2020

Significant detection of video can more rationally allocate computing resources and reduce the amount of computation to improve accuracy. Deep learning can extract the edge features of the image, providing technical support for video saliency. This paper proposes a new detection method. We combine the Convolutional Neural Network (CNN) and the Deep Bidirectional LSTM Network (DB-LSTM) to learn the spatio-temporal features by exploring the object motion information and object motion information to generate video. A continuous frame of significant images. We also analyzed the sample database and found that human attention and significant conversion are time-dependent, so we also considered the significance detection of video cross-frame. Finally, experiments show that our method is superior to other advanced methods.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.27-34
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• 2004

This paper proposes a method to trace and interpret a moving object based on the information which can be directly obtained from MPEG-2 compressed video stream without decoding process. In the proposed method, the motion flow is constructed from the motion vectors included in compressed video. We calculate the amount of pan, tilt, and zoom associated with camera operations using generalized Hough transform. The local object motion can be extracted from the motion flow after the compensation with the parameters related to the global camera motion. Initially, a moving object to be traced is designated by user via bounding box. After then automatic tracking Is performed based on the accumulated motion flows according to the area contributions. Also, in order to reduce the cumulative tracking error, the object area is reshaped in the first I-frame of a GOP by matching the DCT coefficients. The proposed method can improve the computation speed because the information can be directly obtained from the MPEG-2 compressed video, but the object boundary is limited by macro-blocks rather than pixels. Also, the proposed method is proper for approximate object tracking rather than accurate tracing of an object because of limited information available in the compressed video data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.147-153
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• 1998

This paper provides a method for video augmentation using image interpolation. In computer graphics or augmented reality, 3D information of a model object is necessary to generate 2D views of the model, which are then inserted into or overlayed on environmental views or real video frames. However, we do not require any three dimensional model but images of the model object at some locations to render views according to the motion of video camera which is calculated by an SFM algorithm using point matches under weak-perspective (scaled-orthographic) projection model. Thus, a linear view interpolation algorithm is applied rather than a 3D ray-tracing method to get a view of the model at different viewpoints from model views. In order to get novel views in a way that agrees with the camera motion the camera coordinate system is embedded into model coordinate system at initialization time on the basis of 3D information recovered from video images and model views, respectively. During the sequence, motion parameters from video frames are used to compute interpolation parameters, and rendered model views are overlayed on corresponding video frames. Experimental results for real video frames and model views are given. Finally, discussion on the limitations of the method and subjects for future research are provided.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1560-1571
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• 2006

In order to transmit multimedia streams over the network with a timely changing channel bandwidth such as Internet, scalable video coding schemes have been studied to represent video in flexible bitstream. Much research has been made on how to represent encoded media(such as video) bitstream in scalable ways. In this paper, rte propose an optimal selection of the objects for MPEG-4 bitstream adaptation to meet a given constraint. We adopt a multiple choice knapsack problem with multi-step selection for the MPEG-4 objects with different bit-rate scaling levels in the MPEG-4 bitstream. The bitstream adaptation based on the optimal selection result is then to fetch the necessary parts of the MPEG-4 bitstream to constitute an adapted version of the original MPEG-4 binary resource. The experiment results show that the optimal selection of MPEG-4 objects for a given constraint can promisingly be made which meets the given constraint.