Kalman Filter Tracking

Ali, Nasser; Hassan, Ghassan M.

doi:10.5120/15530-4315

Cited by 25 publications

(12 citation statements)

References 13 publications

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“…It is considered that the vehicle miss detection causes the trajectory matching failure. If the value is less than the response threshold of 0.5, it is determined that occlusion is currently occurring, the position of the occlusion object is predicted by the Kalman filter algorithm ( 15 ), and the state of the k th frame is predicted by the state value of the k –1th frame. When the vehicle leaves the obstruction, the vehicle is re-detected, and the data association method is used again to make the vehicle object and the trajectory match successfully, and the tracking is continued.…”

Section: Methodsmentioning

confidence: 99%

“…Object tracking is an important task of any intelligent transportation system (ITS). Past research on single-object tracking has mainly used statistical methods and feature point matching, such as mean shift tracking algorithm ( 14 ) or Kalman filter algorithm ( 15 ). The Kalman filter algorithm ( 15 ) predicts the object’s position by describing the state of the object to track the object.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Past research on single-object tracking has mainly used statistical methods and feature point matching, such as mean shift tracking algorithm ( 14 ) or Kalman filter algorithm ( 15 ). The Kalman filter algorithm ( 15 ) predicts the object’s position by describing the state of the object to track the object. The minimum output sum of squared error filter (MOSSE) ( 16 ) algorithm uses the correlation filter for object tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

Liang

Song

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Using deep learning technology and multi-object tracking method to count vehicles accurately in different traffic conditions is a hot research topic in the field of intelligent transportation. In this paper, first, a vehicle dataset from the perspective of highway surveillance cameras is constructed, and the vehicle detection model is obtained by training using the You Only Look Once (YOLO) version 3 network. Second, an improved multi-scale and multi-feature tracking algorithm based on a kernel correlation filter (KCF) algorithm is proposed to avoid the KCF extracting single features and single-scale defects. Combining the intersection over union (IoU) similarity measure and the row-column optimal association criterion proposed in this paper, matching strategy is used to process the vehicles that are not detected and wrongly detected, thereby obtaining complete vehicle trajectories. Finally, according to the trajectory of the vehicle, the traveling direction of the vehicle is automatically determined, and the setting position of the detecting line is automatically updated to obtain the vehicle count result accurately. Experiments were conducted in a variety of traffic scenes and compared with published data. The experimental results show that the proposed method achieves high accuracy of vehicle detection while maintaining accuracy and precision in tracking multiple objects, and obtains accurate vehicle counting results which can meet real-time processing requirements. The algorithm presented in this paper has practical application for vehicle counting in complex highway scenes.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

Liang

Song

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…To resolve the problems caused by the circular shift and cosine window filter, we define the importance of negative training samples by a Gaussian function, and predict the position of the detection target by a Kalman filter [ 54 , 55 , 56 ].…”

Section: Tracking Algorithm For the Moving Targetmentioning

confidence: 99%

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

et al. 2021

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This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system.

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“…A simple Kalman Filter 35 was added to reduce measurement noise. Constant accelerations both parallel and perpendicular to the direction of motion were assumed.…”

Section: Image Processingmentioning

confidence: 99%

Camera-based measurement of cyclist motion

Eddy

Saxe

Cebon

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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Heavy Goods Vehicles (HGVs) are overrepresented in cyclist fatality statistics in the UK relative to their proportion of total traffic volume. In particular, the statistics highlight a problem for vehicles turning left across the path of a cyclist on their inside. In this paper we present a camera-based system to detect and track cyclists in the blind spot. The system uses boosted classifiers and geometric constraints to detect cyclist wheels, and Canny edge detection to locate the ground contact point. The locations of these points are mapped into physical coordinates using a calibration system based on the ground plane. A Kalman Filter is used to track and predict the future motion of the cyclist. Full-scale tests were conducted using a construction vehicle fitted with two cameras, and the results compared with measurements from an ultrasonic-sensor system. Errors were comparable to the ultrasonic system, with average error standard deviation of 4.3 cm when the cyclist was 1.5 m from the HGV, and 7.1 cm at a distance of 1 m. When results were compared to manually extracted cyclist position data, errors were less than 4 cm at separations of 1.5 m and 1 m. Compared to the ultrasonic system, the camera system requires simpler hardware and can easily differentiate cyclists from stationary or moving background objects such as parked cars or roadside furniture. However, the cameras suffer from reduced robustness and accuracy at close range, and cannot operate in low-light conditions.

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Kalman Filter Tracking

Cited by 25 publications

References 13 publications

Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

Camera-based measurement of cyclist motion

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