Aligned Spatial-Temporal Memory Network for Thermal Infrared Target Tracking

Shu, Xiu; Li, Qiao; He, Zhenyu

doi:10.1109/tcsii.2022.3223871

Cited by 32 publications

(15 citation statements)

References 43 publications

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“…The novel FAAM subnetwork incorporated into the tracking system plays a crucial role in improving performance [27] . Yuan et al (2022) developed an Aligned Spatial-Temporal Memory network-based Tracking method (ASTMT) for thermal infrared (TIR) target tracking. This research focuses on the challenges of occlusion and similarity interference in TIR target tracking and proposes a spatial-temporal memory network to effectively store scene information and decrease interference, thereby enhancing detection accuracy in complex scenarios [28].…”

Section: Related Workmentioning

confidence: 99%

“…Yuan et al (2022) developed an Aligned Spatial-Temporal Memory network-based Tracking method (ASTMT) for thermal infrared (TIR) target tracking. This research focuses on the challenges of occlusion and similarity interference in TIR target tracking and proposes a spatial-temporal memory network to effectively store scene information and decrease interference, thereby enhancing detection accuracy in complex scenarios [28]. Gu et al (2022) presented RPformer, a robust parallel transformer for visual tracking in complex scenes.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Muzammul,

Algarni,

Ghadi

et al. 2024

IEEE Access

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This research presents a groundbreaking approach in aerial image analysis by integrating the Real-Time Detection and Recognition (RT-DETR-X) model with the Slicing Aided Hyper Inference (SAHI) methodology, utilizing the VisDrone-DET dataset. Aimed at enhancing the efficiency of drone technology across a spectrum of applications, including water conservancy, geological exploration, and military operations, this study focuses on harnessing the real-time, end-to-end object detection capabilities of RT-DETR-X. Characterized by its high-speed and high-accuracy performance, particularly in UAV aerial photography, RT-DETR-X demonstrates a remarkable 54.8% Average Precision (AP) and 74 frames per second (FPS), surpassing similar models in both speed and accuracy. The research thoroughly examines the VisDrone-DET dataset, which encompasses a diverse range of small targets in UAV aerial photography scenes. Covering 10 distinct categories, the dataset provides a robust platform for rigorous model testing. The study emphasizes the utilization of the original image dataset for comprehensive training and evaluation, alongside the practical implementation of the SAHI method for enhanced detection of small-scale objects. Through an in-depth exploration of the model's performance in various scenarios and a detailed analysis of the environmental setup, this paper underscores the impact of integrating RT-DETR with the SAHI approach. The findings reveal significant progress in drone detection technologies, offering a holistic framework for effective and efficient aerial surveillance. The integration not only boosts the model's detection accuracy but also opens new avenues for advanced image analysis in UAV applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Muzammul,

Algarni,

Ghadi

et al. 2024

IEEE Access

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show abstract

“…While existing research in the field of infrared object detection and tracking has made significant strides, much of it has focused either on detection [3][4][5] or tracking [6][7][8] in isolation. Furthermore, studies specifically addressing infrared tracking in complex environments, such as dense urban traffic scenarios, are relatively scarce.…”

Section: Introductionmentioning

confidence: 99%

Infrared multi‐target detection and tracking in dense urban traffic scenes

Zha,

Luo,

2024

IET Image Processing

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Infrared object detection and tracking in dense urban traffic remain a challenge due to factors such as low contrast, small intra‐class differences, and frequent false positives and negatives. To overcome these, the authors introduce YOLO‐IR, an algorithm based on the enhanced YOLOv8s, and YOLO‐DeepOC‐IR, a comprehensive infrared multi‐object tracking method for urban traffic, integrating both detection and tracking. During preprocessing, three infrared image enhancement techniques, local contrast multi‐scale enhancement, non‐local means, and contrast limited adaptive histogram equalization, are applied for better reliability in dense scenes. To further improve the performance, the original YOLOv8s backbone is replaced with MobileVITv3 to enhance detection accuracy and robustness. This infrared feature extraction module, incorporated into the detector, combines canny edge detection, Gabor filtering, and open operation layers, significantly boosting object detection in infrared imagery. The tracker's feature processing capabilities are improved using the learned arrangements of three patch codes descriptor and locality‐sensitive hashing for feature extraction and matching. Experimental results on FLIR ADAS v2 and InfiRay datasets indicate superior performance of this method, achieving 78.6% mAP and 151.1 FPS in detection, and up to 80.8% moving object tracking accuracy, 78.6% identification F1 score, and 62.1% higher order tracking accuracy in multi‐object tracking.

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“…Shang et al [11] added an occlusion prediction branch to Siamese-based network to detect the target occlusion degree and correct the target prediction position. Yuan et al [12] introduced a spatial-temporal memory network and an aligned matching model aimed at alleviating the impact of distractors and occlusion. While there have been some advancements in addressing challenges related to target occlusion, these methods are insufficient to effectively tackle severe or complete occlusion challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Tracking by multi‐agent reinforcement learning‐based beam search (BeamTracking [15]) proposed a beam search strategy based on multi‐agent reinforcement learning to address occlusion and fast motion issues. Differing from the methods mentioned above, which focus on enhancing target feature discrimination [6, 8–10, 12, 13], and approaches based on multi‐frame target matching [14‐16], this paper employs a concise strategy involving occlusion state detection and trajectory prediction to effectively address the issue of occlusion.…”

Section: Introductionmentioning

confidence: 99%

AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

Ma,

Yang,

Chen

2024

IET Image Processing

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To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module.

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Aligned Spatial-Temporal Memory Network for Thermal Infrared Target Tracking

Cited by 32 publications

References 43 publications

Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Infrared multi‐target detection and tracking in dense urban traffic scenes

AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

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