Interacting Multiple Model LK Tracking

2014 ◽  
Vol 644-650 ◽  
pp. 1733-1736
Author(s):  
Hong Wang ◽  
Jia Deng
Keyword(s):  
Minimum Variance ◽  
Moving Object ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Object State ◽  
Motion Models ◽  
Nonlinear Motion ◽  
Tracking Process ◽  
Feature Based ◽  
Object Features

The nonlinear motion state of object seriously affects the object tracking characteristics in complex motion scene. In this paper, we propose an interacting multiple model LK (IMM-LK) tracking algorithm to enhance the performance of tracking nonlinear moving object. LK tracking approach is based on the localized gradient obtaining stable optical-flow feature, based on LK, we build several motion models of the tracked object that interact with each other in the tracking process. The method extracts different model's object features, estimates the object state and calculates the matching rate of each model with the current motion model using theory of minimum variance. Combining with the optimal transfer matrix then we can track the nonlinear moving object. The proposed IMM-LK algorithm performs favorably against conventional LK tracking on the performance of tracking nonlinear moving object.

scholarly journals An approach of constant false alarm ratio for improved target tracking

2020 ◽  
Vol 70 (3) ◽  
pp. 17-23
Author(s):  
Zvonko Radosavljević ◽  
Dejan Ivković
Keyword(s):  
False Alarm ◽  
Tracking Error ◽  
Probability Of Detection ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Moving Targets ◽  
False Alarm Ratio ◽  
Motion Models ◽  
Areas Of Interest

Each radar has the function of surveillance of certain areas of interest. In particular, the radar also has the function of tracking moving targets in that territory with some probability of detection, which depends on the type of detector. Constant false alarm ratio (CFAR) is a very commonly used detector. Changing the probability of target detection can directly affect the quality of tracking the moving targets. The paper presents the theoretical basis of the influence of CFAR detectors on the quality of tracking, as well as an approach to the selection of CFAR detectors, CATM CFAR, which enables better monitoring by the Interacting Multiple Model (IMM) algorithm with two motion models. Comparative analysis of CA and CATM algorithm realized by numerical simulations has shown that CATM CFAR gives less tracking error with proportionally the same computer resources.

The Application of IMM-PF in Airport Surface Moving Target Tracking

2014 ◽  
Vol 1056 ◽  
pp. 240-243
Author(s):  
Qian Chen ◽  
Bang Feng Wang ◽  
Shu Lin Liu
Keyword(s):  
Target Tracking ◽  
Moving Target ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Moving Targets ◽  
Constant Acceleration ◽  
Moving Target Tracking ◽  
Motion Models ◽  
Ca Model ◽  
Cv Model

In order to improve the accuracy of surveillance for the airport surface moving targets, the interacting multiple model (IMM) algorithm, adopting three motion models including the constant velocity (CV) model, the constant acceleration (CA) model and the constant turning (CT) model, is combined with the particle filter (PF) algorithm. Besides, the airport map information is utilized to amend the measured data and the output estimates so as to further improve the accuracy of airport surface moving target tracking. Numerical simulations show that the improved algorithm described in this paper is more feasible and effective in tracking the airport surface moving targets.

scholarly journals An Improved Interacting Multiple Model Algorithm Used in Aircraft Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wei dong Zhou ◽  
Jia nan Cai ◽  
Long Sun ◽  
Chen Shen
Keyword(s):  
Transition Matrix ◽  
Continuous System ◽  
Minimum Variance ◽  
Interacting Multiple Model ◽  
Mode Transition ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Sojourn Times ◽  
Aircraft Tracking ◽  
Model Probability

There are some problems in traditional interacting multiple model algorithms (IMM) when used in target tracking systems. For instance, the mode transition matrix is inaccurate and cannot be determined when the sojourn times are not known. To solve these problems, an optimal mode transition matrix IMM (OMTM-IMM) algorithm is proposed in this paper. The linear minimum variance theory is used to calculate the mode transition matrix which depends on the continuous system state rather than the sojourn times in this algorithm. Moreover, the correlation of the subfilter is considered; hence the covariance matrices are utilized to compute mode transition matrix. In this algorithm, the model probability is defined as a diagonal matrix which is combined with the filters outputs; thus the effects produced by each state can be distinguished. Finally, to verify the superiority of the new algorithm, the theoretical proof and simulation results are given. They show that the OMTM-IMM algorithm can improve the tracking accuracy and can be utilized in the complex environment.

scholarly journals Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 347
Author(s):  
Máté Kolat ◽  
Olivér Törő ◽  
Tamás Bécsi
Keyword(s):  
Performance Evaluation ◽  
Kalman Filtering ◽  
Classification Algorithm ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Simulation Environment ◽  
Model Framework ◽  
Motion Models ◽  
Environment Perception

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.

scholarly journals Independent storage of real-world object features is visual rather than verbal in nature

2021 ◽  
Author(s):  
Platon Tikhonenko ◽  
Timothy F. Brady ◽  
Igor Utochkin
Keyword(s):  
Real World ◽  
Long Term Memory ◽  
Study Phase ◽  
Term Memory ◽  
Object State ◽  
Feature Based ◽  
Object Features ◽  
High Degree ◽  
State Configuration

Previous work has shown that semantically meaningful properties of visually presented real-world objects, such as their color, their state/configuration of their parts/pose, or the features that differentiate them from other exemplars of the same category category, are stored with a high degree of independence in long-term memory (e.g., are frequently swapped or misbound across objects). But is this feature independence due to the visual representation of the objects, or because of verbal encoding? Semantically meaningful features can also be labeled by distinct words, which can be recombined to produce independent descriptions of real-world object features. Here, we directly test how much of the pattern of feature independence arises from visual vs. verbal encoding. In two experiments, during the study phase we orthogonally varied the match or mismatch of state (e.g., open/closed) and color information between images of objects and their verbal descriptions (Experiment 1) or between images of two exemplars from the same category (Experiment 2). At test, observers had to choose a previously presented image or description in a 4-AFC task. Whereas in Experiment 1 we found quite a small effect of visual-verbal mismatch on memory for images, the effect of mismatch between exemplars in Experiment 2 was dramatic: memory for a feature was reasonably good when it matched between exemplars, but dropped to chance otherwise. Importantly, this effect was observed both for color and object state independently. We conclude that independent, feature-based storage of objects in long-term memory is provided primarily by visual representations with possible minor influences of verbal encoding.

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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