Multiple-target tracking and track management for an FMCW radar network

Kim, Dae-Bong; Hong, Sungjun

doi:10.1186/1687-6180-2013-159

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…The main function of track management is to manage the generation, maintenance, and disappearance of the track. Track management can solve the problem of false measurement and missing target detection [20]. The measurement target that is not associated with any track is a generated temporary track.…”

Section: Tracking Processing Modulementioning

confidence: 99%

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Chen

Pei

Chen

2019

Sensors

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Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors’ local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.

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Section: Tracking Processing Modulementioning

confidence: 99%

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Chen

Pei

Chen

2019

Sensors

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“…Multiple target tracking and track management using a radar network in cluttered environment was discussed by Kim et al. 24 They used frequency measurements and associate them directly to tracks. This scheme eliminated range/range-rate.…”

Section: Introductionmentioning

confidence: 99%

Occluded human recognition for a leader following system using 3D range and image data in forest environment

et al. 2014

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This paper describes the occluded target recognition and tracking method for a leader-following system by fusing 3D range and image data acquired from 3D light detection and ranging (LIDAR) and a color camera installed on an autonomous vehicle in forest environment. During 3D data processing, the distance-based clustering method has an instinctive problem in close encounters. In the tracking phase, we divide an object tracking process into three phases based on occlusion scenario; before an occlusion (BO) phase, a partially or fully occlusion phase and after an occlusion (AO) phase. To improve the data association performance, we use camera's rich information to find correspondence among objects during above mentioned three phases of occlusion. In this paper, we solve a correspondence problem using the color features of human objects with the sum of squared distance (SSD) and the normalized cross correlation (NCC). The features are integrated with derived windows from Harris corner. The experimental results for a leader following on an autonomous vehicle are shown with LIDAR and camera for improving a data association problem in a multiple object tracking system.

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“…In multi‐target tracking problems, the number of individual targets changes over time due to the targets appearing and disappearing in the scene. Therefore, track management techniques should be employed to initiate, delete, or maintain tracks based on some criteria [6, 7].…”

Section: Introductionmentioning

confidence: 99%

Automotive radar system for multiple‐vehicle detection and tracking in urban environments

Eltrass

Khalil

2018

IET Intelligent Transport Systems

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Multiple-target tracking and track management for an FMCW radar network

Cited by 18 publications

References 13 publications

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Occluded human recognition for a leader following system using 3D range and image data in forest environment

Automotive radar system for multiple‐vehicle detection and tracking in urban environments

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