Anti-interference small target tracking from infrared dual waveband imagery

Qian, Kun; Rong, Sheng Hui; Cheng, Kuan Hong

doi:10.1016/j.infrared.2021.103882

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…To fully evaluate the performance of the proposed algorithms, some related IR trackers, including KCFD [32], SiamIST [38], STCGIF [52], KCF [16], KCFCF [25], and STRCF [18] are used to compare with STRCF2 (in section III-C4) and STRCFD. It should be noted that the results of the STRCF2GIF method and the STRCF2LCM method prove the performance of the GLC model, which is described in the ablation study section.…”

Section: Resultsmentioning

confidence: 99%

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STRCFD: Small Maneuvering Object Tracking via Improved STRCF and Redetection in Near Infrared Videos

Qian,

Wang,

Zhang

2024

IEEE Access

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Tracking small objects in infrared videos is challenging due to a complex background, weak information and target mobility. To deal with these difficulties, an infrared small target tracking algorithm is proposed, which utilizes the Spatial-Temporal Regularized Correlation Filter (STRCF) as the backbone. First, the local image patch that refers to the target and its neighborhood background is given in the STRCF. Then, the guided local contrast mechanism is designed to eliminate noise and distinguish the target from the background. Furthermore, the robustness of the tracking is improved by using the detection model with an adaptive factor of search range, helping to alleviate the problem of tracking migration. Experimental results on entire public near-infrared videos show the superior performance of the proposed algorithm (named STRCFD), compared to several related algorithms in visual effects and objective evaluation. It should be mentioned that the proposed STRCFD achieves an overall precision of 81.1%.

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Section: Resultsmentioning

confidence: 99%

“…Qian et al first attempted to incorporate detectors in small target tracking. Nevertheless, the Kernelized Correlation Filter and Detection (KCFD) method [32] by pipeline filtering is time-consuming. Here, the feature enhancement method and the target redection method encourage us to improve the performance of small target tracking.…”

Section: Introductionmentioning

confidence: 99%

STRCFD: Small Maneuvering Object Tracking via Improved STRCF and Redetection in Near Infrared Videos

Qian,

Wang,

Zhang

2024

IEEE Access

Self Cite

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“…Step 4: Trajectory detection. The trajectory p (t,l) traj |1 ≤ t ≤ N can be generated by performing the moving pipeline filtering [30] on the coordinate set p ( * t,l) |1 ≤ t ≤ N .…”

Section: Multi-target Detectionmentioning

confidence: 99%

“…In Fig. 5, the straight-line trajectory 1 is marked in red; its initial coordinates were (5, 10), and the motion velocity was (1.2, 1.1) pixel/frame; the straight-line trajectory 2 is marked in blue; its initial coordinates were (30,20), and the motion velocity was (0.6, 0.8) pixel/ frame; the arc trajectory is marked in green; its motion speed of 1 pixel/frame, arc center coordinates of (64, 64), and an arc radius of 20 pixels.…”

Section: Single-target Detection Testmentioning

confidence: 99%

Dynamic programming network for point target detection

Wei

2023

EURASIP J. Adv. Signal Process.

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To improve the efficiency of the dim point target detection based on dynamic programming (DP), this paper proposes a multi-frame target detection method based on a DP ring network (DPRN). In the proposed method, first, the target trajectory is approximated using the piecewise linear function. The velocity space partition DP (VSP-DP) is used to accumulate the merit functions of a target on each piecewise linear trajectory segment to avoid the merit function diffusion in different velocity spaces. In addition, the velocity space matching DP (VSM-DP) is employed to realize the state transition of a target between adjacent piecewise linear trajectory segments. Then, the VSP-DP and VSM-DP are used to construct a DP network (DPN). Second, to suppress the merit function diffusion further, the sequential and reverse DPNs are connected in a head-to-tail manner to form a DPRN, and the merit function of the DPRN is obtained by averaging the merit functions of the sequential and reverse DPNs. Finally, the target trajectory is obtained by tracking the extreme points of the merit functions of the DPRN. The simulation and analysis results show that the proposed DPRN combines the advantages of high detection probability of the high-order DP and high execution efficiency of the first-order DP. The proposed DPRN is suitable for radars and infrared searching and tracking systems.

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“…Recently, scholars have proposed many tracking algorithms to meet the requirements of the dim and small target tracking, which can be divided into two main categories including correlation filters-based methods [12][13][14] and PF-based methods [15][16][17][18][19]. KCF has received the most attention in the methods based on correlation filters for dim and small target tracking, which is achieved by establishing a discriminator based on the correlation operator with a kernel function.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

et al. 2022

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Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

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Anti-interference small target tracking from infrared dual waveband imagery

Cited by 11 publications

References 31 publications

STRCFD: Small Maneuvering Object Tracking via Improved STRCF and Redetection in Near Infrared Videos

STRCFD: Small Maneuvering Object Tracking via Improved STRCF and Redetection in Near Infrared Videos

Dynamic programming network for point target detection

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

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