Aerial Infrared Target Tracking in Complex Background Based on Combined Tracking and Detecting

Hu, Yangguang; Xiao, Mingqing; Zhang, Kai; Wang, Xiaotian

doi:10.1155/2019/2419579

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…The combination of UAV with infrared equipment can solve the tracking problem of weak targets and hidden targets [ 21 ]. However, due to the high data feature dimensions, it is not suitable for the tracking analysis of fast-moving targets and exhibits low real-time performance.…”

Section: Traditional Target Tracking Algorithmmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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Section: Traditional Target Tracking Algorithmmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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“…Many of them have been evaluated with the Visual Tracker Benchmark [23], [24] and in the VOT challenge [25]. We restrict our review here to those tracking algorithms that are close to our work, including Struck [26], TLD [27], MOSSE [12], CSK [28], KCF [13], CN [29], DSST [15], SAMF [16], BACF [30], SKCF [31], OMFL [32], C-COT [33], ECO [34], LCT [19], LMCF [34], CA-CF-SVM [35], and CTAD [36].…”

Section: A Tracking Algorithmsmentioning

confidence: 99%

An Optimal Long-Term Aerial Infrared Object Tracking Algorithm With Re-Detection

Wang

Zhang

et al. 2019

IEEE Access

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In the field of automatic target recognition and tracking, long-term tracking for aerial infrared target has been recently seen with great interest. Although deep trackers and correlation filtering trackers offer competitive results on performance, the problems of deformation, abrupt motion, heavy occlusion, and out of view still remain unsolved. In addition, since this paper focus on infrared images, it is also important to consider that infrared images have a significant drawbacks, such as low resolution, low contrast, and lack of textures. In this paper, we adopt correlation filtering trackers and deep learning detection method to achieve accurate tracking results. Our tracking system composed of three parts: the DTB correlation filtering tracker (DTB-CF), a better regression model to discriminate the target from the background with adjustable Gaussian window functions; the UTA correlation filtering tracker (UTA-CF), an optimum regression model to update the target appearance with simultaneously optimal in position, scale, and integration of multi-feature fusion; and the YOLOv3 re-detector, which ensures re-location of the correct position of the target when the tracking fails. In addition, we introduce the ratio between average peak-to-correlation energy (APCE) of the current frame and average APCE of former frames as a criterion to update the UTA-CF tracker to maintain the target model stability. And we combine the nearest neighbor maximum value method with APCE as criterion together to initialize the YOLOv3 re-detector. We evaluate our algorithm on real aerial infrared target thermal image sequences in terms of precision plot, success plot, and speed. The experimental results show that our method has a significant improvement than the state-of-the-art methods for long-term tracking both in accuracy and robustness for aerial infrared object tracking. INDEX TERMS Aerial infrared object tracking, correlation filtering, deep learning detection, multi-feature fusion, APCE criterion. The associate editor coordinating the review of this manuscript and approving it for publication was Shangce Gao. we focus on the problem of long-term aerial infrared object tracking. Compared with visual tracking, infrared object tracking is more challenging. It needs not only to solve the problem of universal tracking (e.g., deformation, abrupt motion, heavy occlusion and out of view), but also to consider its significant defects with low resolution, low contrast and lack of textures. An effective and real-time tracking algorithm should be able to consistently track the infrared object for a long time without failing under these situations. At present, the mainstream methods of tracking algorithms are based on two types: the first is the traditional correlation filtering method, and the other is the convolutional neural network method. The convolutional neural network method has powerful capability of feature extraction.

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“…Despite the many advantages of infrared target tracking, the challenges it faces are equally great. The fundamental reason is that the imaging principle of infrared images makes infrared images have serious low resolution, large background noise, blurred images, and missing texture compared to RGB images [14], etc. At the same time, infrared target tracking also faces some of the same problems as RGB tracking, such as object deformation, occlusion, scale transformation, and object drift, etc., which lead to the poor quality of feature maps obtained in the process of feature extraction of infrared images and is not conducive to the subsequent tracking task.…”

Section: Introductionmentioning

confidence: 99%

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

Li,

Diao,

Fang

et al. 2023

Preprint

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Due to the imaging specificity of infrared images, compared with the RGB images, there are drawbacks such as blurring, low contrast, unclear texture details and large background noise. So the tracking is more difficult and challenging. There are two main methods for infrared target tracking. One is to use Handcrafted features, but it is time-consuming and costly. The other is infrared target tracking algorithm which is the current mainstream algorithm and is to apply the tracking model used for RGB tracking to infrared target tracking. Compared with the RGB images, the image features extracted from infrared images often have low quality and poor tracking performance. Therefore, to address these problems, based on the idea of Siamese network, an efficient infrared target tracking model SiamMCA is designed in this paper. SiamMCA is mainly composed of a feature extraction network module, a mixed channel feature enhancement module, a multi-scale feature fusion module and a regional regression network module (RPN) for object classification and bounding box regression. Firstly, the feature extraction network module based on the deep residual network Resnet50 extracts richer and more comprehensive feature information by introducing multi-head self-attention. Then the mixed channel feature enhancement module assigns different weights to the features of different channels, which makes the model more focus on the regions of interest. Finally, response maps with multi-scale features are obtained by a multi-scale feature fusion module and are fed into the regional regression network module to classify the target and predict and regress the bounding box. The experimental results show that SiamMCA achieves nice performance on infrared data benchmarks LSOTB-TIR and PTB-TIR, and can effectively cope with various infrared target tracking tasks.

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Aerial Infrared Target Tracking in Complex Background Based on Combined Tracking and Detecting

Cited by 12 publications

References 40 publications

Aerial Video Trackers Review

Aerial Video Trackers Review

An Optimal Long-Term Aerial Infrared Object Tracking Algorithm With Re-Detection

SiamMCA: Infrared Target Tracking Algorithm with Mixed Channel Attention

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