• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.99.3-99
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• 2002

1. Introduction 2. Detection and Tracking of Moving Objects 3. Objects Tracking and Experiment 4. Conclusion

• Journal of Industrial Technology
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• v.21 no.B
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• pp.91-98
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• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.298-301
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• 2012

This paper presents a graph-based method of detecting and tracking moving objects in H.264/SVC bitstreams for video surveillance applications that makes use the information from spatial base and enhancement layers of the bitstreams. In the base layer, segmentation of real moving objects are first performed using a spatio-temporal graph by removing false detected objects via graph pruning and graph projection, followed by graph matching to precisely identify the real moving objects over time even under occlusion. For the accurate detection and reliable tracking of moving objects in the enhancement layer, as well as saving computational complexity, the identified block groups of the real moving objects in the base layer are then mapped to the enhancement layer to provide accurate and efficient object detection and tracking in the bitstreams of higher resolution. Experimental results show the proposed method can produce reliable results with low computational complexity in both spatial layers of H.264/SVC test bitstreams.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.427-435
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• 2002

This paper presents a development result for image processed tracking system of multiple moving objects based on Kalman filter and a simple window tracking method. The proposed algorithm of foreground detection and background adaptation (FDBA) is composed of three modules: a block checking module(BCM), an object movement prediction module(OMPM), and an adaptive background estimation module (ABEM). The BCM is processed for checking the existence of objects. To speed up the image processing time and to precisely track multiple objects under the object's mergence, a concept of a simple window tracking method is adopted in the OMPM. The ABEM separates the foreground from the background in the reset simple tracking window in the OMPM. It is shown through experimental results that the proposed FDBA algorithm is robustly adaptable to the background variation in a short processing time. Furthermore, it is shown that the proposed method can solve the problems of mergence, cross and split that are brought up in the case of tracking multiple moving objects.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.63.5-63
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• 2001

In this paper, moving objects tracking and dynamic characteristic analysis are studied. Kohonen´s self-organizing neural network models are used for moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.586-594
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• 2018

In this paper, we propose a livestock theft detection system through moving object classification and tracking method. To do this, first, we extract moving objects using GMM(Gaussian Mixture Model) and RGB background modeling method. Second, it utilizes a morphology technique to remove shadows and noise, and recognizes moving objects through labeling. Third, the recognized moving objects are classified into human and livestock using skeletal features and color similarity judgment. Fourth, for the classified moving objects, CAM (Continuously Adaptive Meanshift) Shift and Kalman Filter are used to perform tracking and overlapping judgment, and risk is judged to generate a notification. Finally, several experiments demonstrate the feasibility and applicability of the proposed method.

• 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.