• Title/Summary/Keyword: Depth Tracking

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Performance Improvement of Camshift Tracking Algorithm Using Depth Information (Depth 정보를 이용한 CamShift 추적 알고리즘의 성능 개선)

  • Joo, Seong-UK;Choi, Han-Go
    • Journal of the Institute of Convergence Signal Processing
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    • v.18 no.2
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    • pp.68-75
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    • 2017
  • This study deals with a color-based tracking method of a moving object effectively in case that the color of the moving object is same as or similar to that of background. The CamShift algorithm, which is the representative color-based tracking method, shows unstable tracking when the color of moving objects exists in the background. In order to overcome the drawback, this paper proposes the CamShift algorithm merged with depth information of the object. Depth information can be obtained from Kinect device which measures the distance information of all pixels in an image. Experimental result shows that the proposed tracking method, the Camshift merged with depth information of the tracking object, makes up for the unstable tracking of the existing CamShift algorithm and also shows improved tracking performance in comparison with only CamShift algorithm.

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Active Shape Model-based Object Tracking using Depth Sensor (깊이 센서를 이용한 능동형태모델 기반의 객체 추적 방법)

  • Jung, Hun Jo;Lee, Dong Eun
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.9 no.1
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    • pp.141-150
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    • 2013
  • This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.

Enhancement of Common-path Fourier-domain Optical Coherence Tomography using Active Surface Tracking Algorithm (표면 추적 알고리즘을 적용한 공통경로 FD-OCT의 성능개선)

  • Kim, Min-Ho;Kim, Keo-Sik;Song, Chul-Gyu
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.4
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    • pp.639-642
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    • 2012
  • Optical coherence tomography(OCT) can provide real-time and non-invasive subsurface imaging with ultra-high resolution of micrometer scale. However, conventional OCT systems generally have a limited imaging depth range within a depth of only 1-2 mm. To overcome the limitation, we have proposed an active surface tracking algorithm used in common-path Fourier-domain OCT system in order to extend the imaging depth range. The surface tracking algorithm based on the threshold and Savitzky-Golay filter of A-scan data was applied to real-time tracking. The algorithm has controlled a moving stage according to the sample's surface variance in real time. An OCT image obtained by the algorithm clearly show an extended imaging depth range. Consequently, the proposed algorithm demonstrated the potential for improving the conventional OCT systems with limitary depth range.

Tracking Method for Moving Object Using Depth Picture (깊이 화면을 이용한 움직임 객체의 추적 방법)

  • Kwon, Soon-Kak;Kim, Heung-Jun
    • Journal of Korea Multimedia Society
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    • v.19 no.4
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    • pp.774-779
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    • 2016
  • The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.

A Technique of Image Depth Detection Using Motion Estimation and Object Tracking (모션 추정과 객체 추적을 이용한 이미지 깊이 검출기법)

  • Joh, Beom-Seok;Kim, Young-Ro
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.4 no.2
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    • pp.15-19
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    • 2008
  • In this paper, we propose a new algorithm of image depth detection using motion estimation and object tracking. In industry, robots are used for automobile, conveyer system, etc. But, these have much necessary time. Thus, in this paper, we develop the efficient method of image depth detection based on motion estimation and object tracking.

Visual Object Tracking Fusing CNN and Color Histogram based Tracker and Depth Estimation for Automatic Immersive Audio Mixing

  • Park, Sung-Jun;Islam, Md. Mahbubul;Baek, Joong-Hwan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.3
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    • pp.1121-1141
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    • 2020
  • We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

Depth Control of Autonomous Underwater Vehicle Using Robust Tracking Control (강인추적 제어를 이용한 자율 무인 잠수정의 심도제어)

  • Chai, Chang-Hyun
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.20 no.4
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    • pp.66-72
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    • 2021
  • Since the behavior of an autonomous underwater vehicle (AUV) is influenced by disturbances and moments that are not accurately known, the depth control law of AUVs must have the ability to track the input signal and to reject disturbances simultaneously. Here, we proposed robust tracking control for controlling the depth of an AUV. An augmented closed-loop system is represented by an error dynamic equation, and we can easily show the asymptotic stability of the overall system by using a Lyapunov function. The robust tracking controller is consisted of the internal model of the command signal and a state feedback controller, and it has the ability to track the input signal and reject disturbances. The closed-loop control system is robust to parameter uncertainties. Simulation results showed the control performance of the robust tracking controller to be better than that of a P + PD controller.

Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images

  • Kim, Joongrock;Yoon, Changyong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.2
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    • pp.131-139
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    • 2016
  • Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

3D Multiple Objects Detection and Tracking on Accurate Depth Information for Pose Recognition (자세인식을 위한 정확한 깊이정보에서의 3차원 다중 객체검출 및 추적)

  • Lee, Jae-Won;Jung, Jee-Hoon;Hong, Sung-Hoon
    • Journal of Korea Multimedia Society
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    • v.15 no.8
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    • pp.963-976
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    • 2012
  • 'Gesture' except for voice is the most intuitive means of communication. Thus, many researches on how to control computer using gesture are in progress. User detection and tracking in these studies is one of the most important processes. Conventional 2D object detection and tracking methods are sensitive to changes in the environment or lights, and a mix of 2D and 3D information methods has the disadvantage of a lot of computational complexity. In addition, using conventional 3D information methods can not segment similar depth object. In this paper, we propose object detection and tracking method using Depth Projection Map that is the cumulative value of the depth and motion information. Simulation results show that our method is robust to changes in lighting or environment, and has faster operation speed, and can work well for detection and tracking of similar depth objects.

A Fast and Accurate Face Tracking Scheme by using Depth Information in Addition to Texture Information

  • Kim, Dong-Wook;Kim, Woo-Youl;Yoo, Jisang;Seo, Young-Ho
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.707-720
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    • 2014
  • This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.