Multi-Frame Track-Before-Detect Algorithm for Maneuvering Target Tracking

Yi, Wei; Fang, Zicheng; Li, Wu-Jun; Hoseinnezhad, Reza; Kong, Lingjiang

doi:10.1109/tvt.2020.2976095

Cited by 109 publications

(35 citation statements)

References 50 publications

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“…But it would cause missed detection and a large number of false alarms when the targets have not strong gray value. Wang J et al use dynamic programming algorithm (DP-TBD) to carry out path integral for the target intensity, and use multiframe accumulation to carry out noise reduction processing and target enhancement at the same time [10,11]. For using the value function to carry out the optimal accumulation at each moment to search for the global strategy combination, DP-TBD assumes that the gray value of the target on the full path maintains maximum value.…”

Section: B Multi-frame-basedmentioning

confidence: 99%

“…In order to reflect the effectiveness of the proposed model, we compare dynamic programming algorithm (DP-TBD) [11], time variance filter algorithm (TVF-TBD) [9], Kalman Filtering (KF-TBD) [8], Spatial-Temporal Local Difference (STLDM) algorithm [21] in this paper. The above algorithms cover the mainstream of dim and small target detection algorithm.…”

Section: Target Detection Performance Experimentsmentioning

confidence: 99%

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Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Liu

Li²,

et al. 2021

IEEE Access

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Infrared dim and small target detection is widely used in military and civil fields. Traditional methods in that application rely on the local contrast between the target and background for single-frame detection. On the other hand, those algorithms depend on the motion model with fixed parameters for multiframe association. For the great similarity of gray value and the dynamic changes of motion model parameters in the condition of low SNR and strong clutter, those methods possess weak robustness, low detection probability, and high false alarm rate. In this paper, an infrared video sequences encoding and decoding model based on Bidirectional Convolutional Long Short-Term Memory structure (Bi-Conv-LSTM) and 3D Convolutional structure (3D-Conv) is proposed, addressing the problem of high similarity and dynamic changes of parameters. For solving the problem of dynamic change in parameters, Bi-Conv-LSTM structure is used to learn the motion model of targets. And for the problem of low local contrast, 3D-Conv structure is adopted to extend receptive field in the time dimension. In order to improve the precision of detection, the Decoding part is divided into two different full connection with distinctive active function. Simulation results show that the trajectory detection accuracy of the proposed model is more than 90% under the condition of low SNR and maneuvering motion, which is better than traditional method with 80% in DB-TBD 20% in others. Real data experiment illustrate that that our proposed method can detect small infrared targets with a low false alarm rate and high detection probability.

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Section: B Multi-frame-basedmentioning

confidence: 99%

Section: Target Detection Performance Experimentsmentioning

confidence: 99%

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Liu

Li²,

et al. 2021

IEEE Access

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“…where, a and b are the weights of the HOG-HHG feature response map and the CN feature response map, respectively, max(f HOG-HHG (z)) is the maximum value of the HOG-HHG feature response map, and max(f CN (z)) is maximum value of CN feature response map. The adaptive weights in Equations (14) and (15) can strength the role of the feature response map which has bigger maximum response value. Thus, the main feature can play the more important role to locate the target.…”

Section: Target Detectionmentioning

confidence: 99%

“…In contrast, unsupervised UDT method [12] can resolve the manual labelling problem, however it demands the platform to be very powerful and its tracking accuracy will be degraded. Generally, the tracking accuracy of the methods based on deep learning can be greatly improved, but the large computation cost or workload to label samples constrains the application of these tracking strategies in the real-time detecting and tracking field [13][14][15][16][17][18][19][20][21][22][23][24][25]. In addition, these tracking strategies are also not suitable for tracking target which is outside the training sample set.…”

Section: Introductionmentioning

confidence: 99%

Kernel correlation filter tracking strategy based on adaptive fusion response map

Xiu

2021

IET Image Processing

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Aiming at the problem that the tracking performance of the traditional kernel correlation filter tracking algorithm is easy to be affected by illumination variation, occlusion and motion blur during tracking, an improved tracking strategy is proposed. A new Histogram of Hue Gradient (HHG) feature is designed, and the new HOG-HHG feature is obtained by connecting the HOG and the HHG in series. Two features, CN and HOG-HHG, are extracted respectively, and two kernel correlation filter classifiers are constructed base on the two features above to establish the corresponding response maps of the tracking scenes, respectively. The response maps are fused adaptively to improve the tracking robustness to the complex situations in the tracking process. The updating strategy of the target model is designed based on peak sidelobe ratio (PSR) and its difference, and the adaptive thresholds are used to improve the stability of the target model. Simulation results show that the proposed method has better tracking adaptability to the illumination variation, occlusion and motion blur. Both the precision and the success rate can be enhanced. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In the framework of Bayesian theory, the DP-TBD algorithm estimates the joint posterior probability density function of target states in discrete state space. DP-TBD searches all of the possible paths according to the target motion model, and gradually accumulates the merits of these paths [22], [23]. Common merit functions include posterior density, likelihood ratio and signal amplitude.…”

Section: Introductionmentioning

confidence: 99%

Joint Track-Before-Detect Algorithm for High-Maneuvering Target Indication in Video SAR

Qin

Ding

Wen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Moving target shadows continuously appear in the sequential images produced by video synthetic aperture radar (SAR), which conducts research on shadow-based detection and tracking of moving targets. This paper addresses a typical shadow-based detection and tracking problem of dim highmaneuvering targets in the complex background. Building upon the dynamic programming-based track-before-detect (DP-TBD) algorithm, this paper proposes a joint processing strategy-based DP-TBD (JP-DP-TBD) algorithm to detect and track multiple high-maneuvering targets in video SAR. Benefiting from both of the location and radial velocity information of the targets in the video SAR image and Range-Doppler (RD) spectrum, JP-DP-TBD algorithm can screen the state candidate region and retain more states that conform to the motion law of the target. Meanwhile, we adopt a dual-frame close-range matching mechanism to complete the automatic initialization of the candidate target state, whose validity has been verified on the real video SAR data. Experimental results on simulated data demonstrate that the proposed algorithm has better detection ability and fewer false alarms in comparison with other TBD algorithms.

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Multi-Frame Track-Before-Detect Algorithm for Maneuvering Target Tracking

Cited by 109 publications

References 50 publications

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Kernel correlation filter tracking strategy based on adaptive fusion response map

Joint Track-Before-Detect Algorithm for High-Maneuvering Target Indication in Video SAR

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