Specified Object Tracking in an Environment of Multiple Moving Objects using Particle Filter

Kim, Hyung-Bok; Ko, Kwang-Eun; Kang, Jin-Shig; Sim, Kwee-Bo

doi:10.5391/jkiis.2011.21.1.106

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…The operation principle of the particle filter is very simple [12]. Particle Filter is the most widely used algorithm on tracing object [13]. Particle Filter is one of the Prediction Technique that Sequential Monte Carlo method based on simulation.…”

Section: Particle Filtermentioning

confidence: 99%

Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

Ko¹,

Park²,

Park³

et al. 2012

International Journal of Fuzzy Logic and Intelligent Systems

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This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

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Section: Particle Filtermentioning

confidence: 99%

Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

Ko¹,

Park²,

Park³

et al. 2012

International Journal of Fuzzy Logic and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

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Object Segmentation/Detection through learned Background Model and Segmented Object Tracking Method using Particle Filter

임수창¹,

김도연²

2016

Journal of the Korea Institute of Information and Communication

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In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.키워드 : 배경제거, 배경모델, 객체 분할, 객체 추적, 파티클 필터

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Algorithm Implementation for Detection and Tracking of Ships Using FMCW Radar

Hong¹,

Yang²

2013

Journal of the Korean Society for Marine Environment & Ener

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This study focuses on a ship detection and tracking method using Frequency Modulated Continuous Wave (FMCW) radar used for horizontal surveillance. In general, FMCW radar can play an important role in maritime surveillance, because it has many advantages such as low warm-up time, low power consumption, and its all weather performance. In this paper, we introduce an effective method for data and signal processing of ship's detecting and tracking using the X-band radar. Ships information was extracted using an image-based processing method such as the land masking and morphological filtering with a threshold for a cycle data merged from raw data (spoke data). After that, ships was tracked using search-window that is ship's expected rectangle area in the next frame considering expected maximum speed (19 kts) and interval time (5 sec). By using this method, the tracking results for most of the moving object tracking was successful and those results were compared with AIS (Automatic Identification System) for ships position. Therefore, it can be said that the practical application of this detection and tracking method using FMCW radar improve the maritime safety as well as expand the surveillance coverage cost-effectively. Algorithm improvements are required for an enhancement of small ship detection and tracking technique in the future.

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Specified Object Tracking in an Environment of Multiple Moving Objects using Particle Filter

Cited by 4 publications

References 4 publications

Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

Object Segmentation/Detection through learned Background Model and Segmented Object Tracking Method using Particle Filter

Algorithm Implementation for Detection and Tracking of Ships Using FMCW Radar

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