• Title/Summary/Keyword: moving object tracking

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Multi-objects detection using HOG and effective individual object tracking (HOG를 이용한 다중객체 검출과 효과적인 개별객체 추적)

  • Choi, Min;Lee, Kyu-won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.10a
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    • pp.894-897
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    • 2012
  • We propose a effective method using the HOG (Histogram of Oriented Gradients) feature vector to track individual objects in an environment which multiple objects are moving. The proposed algorithm consists of pre-processing, object detection and object tracking. We experimented with six videos which have various trajectories and the movement. When occlusion between objects was occurred, we identified individual object by using center and predicted coordinates of moving objects. The algorithm shows 85.45% of tracking rate in the videos we experimented. We expect the proposed system is utilized in security systems which require the alalysis of the position and motion pattern of objects.

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Shadow Removal based on Chromaticity and Brightness Distortion for Effective Moving Object Tracking (효과적인 이동물체 추적을 위한 색도와 밝기 왜곡 기반의 그림자 제거)

  • Kim, Yeon-Hee;Kim, Jae-Ho;Kim, Yoon-Ho
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.8 no.4
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    • pp.249-256
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    • 2015
  • Shadow is a common physical phenomenon in natural images and may cause problems in computer vision tasks. Therefore, shadow removal is an essential preprocessing process for effective moving object tracking in video image. In this paper, we proposed the method of shadow removal algorithm using chromaticity, brightness distortion and direction of shadow candidate. The proposed method consists of two steps. First, removal process of primary shadow candidate region by using chromaticity, brightness and distortion. The second stage applies the final shadow candidate region to obtain a direction feature of shadow which is estimated by the thinning algorithm after calculating the lowest pixel position of the moving object. To verify the proposed approach, some experiments are conducted to draw a compare between conventional method and that of proposed. Experimental results showed that proposed methodology is simple, but robust and well adaptive to be need to remove a shadow removal operation.

Continuous Moving Object Tracking Using Query Relaying in Tree-Based Sensor Network (트리 기반의 센서 네트워크에서 질의 중계를 통한 이동 객체의 연속적인 위치 획득 방안)

  • Kim, Sangdae;Kim, Cheonyong;Cho, Hyunchong;Yim, Yongbin;Kim, Sang-Ha
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39B no.5
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    • pp.271-280
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    • 2014
  • In wireless sensor networks, there have been two methods for sensing continuously moving object tracking: user-query based method and periodic report based method. Although the former method requires overhead for user query rather than the latter method, the former one is known as an energy-efficient method without transferring unnecessary information. In the former method, a virtual tree, consisting of sensor nodes, is exploited for the user querying and sensor reporting. The tree stores the information about mobile objects; the stored information is triggered to report by the user query. However, in case of fast moving object, the tracking accuracy reduces due to the time delay of end-to-end repeated query. To solve the problem, we propose a query relaying method reducing the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to the adjacent node according to the movement of mobile object tracking. Relaying the query message reduces the end-to-end querying time delay. Simulation results show that our method is superior to the existing ones in terms of tracking accuracy.

Classification and Tracking of Unknown Multiple Underwater Moving Objects Using Neural Networks (신경망에 의한 미지의 다중 수중 이동물체의 판별 및 추적)

  • 하석운
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.3 no.2
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    • pp.389-396
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    • 1999
  • In this paper, we propose a multiple underwater object classification and tracking algorithm using the narrowband tonal and frequency line features extracted from the frequency spectrum of the acoustic signal. The general algorithm using the wideband and narrowband energy has a high tracking error when objects are close and cross each other. But the proposed algorithm shows a good tracking performance for the simulation scenarios generated by the real acoustic data.

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The development of a visual tracking system for the stable grasping of a moving object (움직이는 물체의 안정한 Grasping을 위한 시각추적 시스템 개발)

  • 차인혁;손영갑;한창수
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.543-546
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    • 1996
  • We propose a new visual tracking system for grasping which can find grasping points of an unknown polygonal object. We construct the system with the image prediction technique and Extended Kalman Filter algorithm. The Extended Kalman Filter(EKF) based on the SVD can improve the accuracy and processing time for the estimation of the nonlinear state variables. By using it, we can solve the numerical unstability problem that can occur in the visual tracking system based on Kalman filter. The image prediction algorithm can reduce the effect of noise and the image processing time. In the processing of a visual tracking, we can construct the parameterized family and can found the grasping points of unknown object through the geometric properties of the parameterized family.

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Vehicle Classification and Tracking based on Deep Learning (딥러닝 기반의 자동차 분류 및 추적 알고리즘)

  • Hyochang Ahn;Yong-Hwan Lee
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.3
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    • pp.161-165
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    • 2023
  • One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

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Moving Object Tracking using Query Relaying in Wireless Sensor Networks (무선 센서 네트워크에서 질의 중계를 이용한 이동 객체의 위치 추적 방안)

  • Kim, Sangdae;Kim, Cheonyong;Cho, Hyunchong;Yim, Yongbin;Kim, Sang-Ha
    • KIISE Transactions on Computing Practices
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    • v.20 no.11
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    • pp.598-603
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    • 2014
  • In wireless sensor networks, two methods have been generally used to track continuously moving object: a user query-based method and a periodic report-based method. Although the former method generates more overhead as a result of the user queries, the former one is also an energy-efficient method that does not transfer unnecessary information. For the user query-based method, a virtual tree that consist of sensor nodes is used to perform the user query and the sensor reporting. The tree stores the information of the mobile objects, and the stored information triggers a report b the user query. However, in case of a fast-moving object, the tracking accuracy decreases as a result of the time delay of the end-to-end repeated query. In order to solve this problem, we propose a query-relay method that reduces the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to adjacent nodes according to the movement of mobile object that is tracked. When the query messages are relayed. The end-to-end querying time delay is reduced. and a simulation shows that our method is superior to existing ones in terms of tracking accuracy.

Improvement of Tracking Performance of Particle Filter in Low Frame Rate Video (낮은 프레임률 영상에서 파티클 필터의 추적 성능 개선)

  • Song, Jong-Kwan
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.2
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    • pp.143-148
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    • 2014
  • Particle filter algorithm has been proven very successful for non-linear and non-Gaussian estimation problem and thus it has been widely used for object tracking for video signals. If the object moves significantly, particle filter needs very large number of particles to track object and this results high computational cost. In this paper, modified particle filter by adopting motion vector is proposed for tracking vehicle in low frame rate(LPR) video input, which the object moving significantly and randomly between consecutive frames. In the proposed algorithm, motion vector is applied in selection and observe step. The experimental result shows that the proposed particle filter can track vehicle successfully in the case when previous one fails. And it also shows the propose method increases the precision of tracking.

Tracking and Interpretation of Moving Object in MPEG-2 Compressed Domain (MPEG-2 압축 영역에서 움직이는 객체의 추적 및 해석)

  • Mun, Su-Jeong;Ryu, Woon-Young;Kim, Joon-Cheol;Lee, Joon-Hoan
    • 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.

Dynamic Object Tracking of a Quad-rotor with Image Processing and an Extended Kalman Filter (영상처리와 확장칼만필터를 이용한 쿼드로터의 동적 물체 추종)

  • Kim, Ki-jung;Yu, Ho-Yun;Lee, Jangmyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.7
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    • pp.641-647
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    • 2015
  • This paper proposes a new strategy for a quad-rotor to track a moving object efficiently by using image processing and an extended Kalman filter. The goal of path planning for the quad-rotor is to design an optimal path from the start point to the destination point. To lengthen the freight time of the quad-rotor, an optimal path is required to reduce the energy consumption. To track a moving object, the mark signed on the moving object has been detected by a camera mounted first on the quad-rotor. The center coordinates of the mark and its area are calculated through the blob analysis which is one type of image processing. The mark coordinates are utilized to obtain information on the motion direction and the area of the mark is utilized to recognize whether the object moves backward or forward from the camera on the quad-rotor. In addition, an extended Kalman filter has been applied to predict the direction and speed of the dynamically moving object. Through these schemes, it is aimed that the quad-rotor can track the dynamic object efficiently in terms of flight distance and time. Through the two different route freights of the quad-rotor, the performance of the proposed system has been demonstrated.