Efficient Online Object Tracking Scheme for Challenging Scenarios

Mehmood, Khizer; Ali, Ahmad; Jalil, Abdul; Khan, Baber; Cheema, Khalid Mehmood; Murad, Maria; Milyani, Ahmad H.

doi:10.3390/s21248481

Cited by 10 publications

(6 citation statements)

References 62 publications

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“…Firstly, the quality of UAV aerial images is affected by many factors, such as the instability of equipment causing jitter, blur, low resolution, light change, image distortion, etc. These problems need to be preprocessed for the video to improve the detection effect of methods [33].…”

Section: The Difference Between Uav Object Detection and Common Objec...mentioning

confidence: 99%

A Survey of Object Detection for UAVs Based on Deep Learning

Tang,

Ni,

Zhao

et al. 2023

Remote Sensing

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With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.

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Section: The Difference Between Uav Object Detection and Common Objec...mentioning

confidence: 99%

A Survey of Object Detection for UAVs Based on Deep Learning

Tang,

Ni,

Zhao

et al. 2023

Remote Sensing

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“…To handle occlusion and deformation robustly, several strategies [40][41][42][43][44] have been used. In deep learning methods, data collection and annotation is the most straightforward way, while it seems impossible to collect data covering all potential occlusion and deformation, even for large-scale datasets.…”

Section: Related Work 21 Occlusion and Deformation Handling In Visual...mentioning

confidence: 99%

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

et al. 2022

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Although the performance of unmanned aerial vehicle (UAV) tracking has benefited from the successful application of discriminative correlation filters (DCF) and convolutional neural networks (CNNs), UAV tracking under occlusion and deformation remains a challenge. The main dilemma is that challenging scenes, such as occlusion or deformation, are very complex and changeable, making it difficult to obtain training data covering all situations, resulting in trained networks that may be confused by new contexts that differ from historical information. Data-driven strategies are the main direction of current solutions, but gathering large-scale datasets with object instances under various occlusion and deformation conditions is difficult and lacks diversity. This paper proposes an attention-based mask generation network (AMGN) for UAV-specific tracking, which combines the attention mechanism and adversarial learning to improve the tracker’s ability to handle occlusion and deformation. After the base CNN extracts the deep features of the candidate region, a series of masks are determined by the spatial attention module and sent to the generator, and the generator discards some features according to these masks to simulate the occlusion and deformation of the object, producing more hard positive samples. The discriminator seeks to distinguish these hard positive samples while guiding mask generation. Such adversarial learning can effectively complement occluded and deformable positive samples in the feature space, allowing to capture more robust features to distinguish objects from backgrounds. Comparative experiments show that our AMGN-based tracker achieves the highest area under curve (AUC) of 0.490 and 0.349, and the highest precision scores of 0.742 and 0.662, on the UAV123 tracking benchmark with partial and full occlusion attributes, respectively. It also achieves the highest AUC of 0.555 and the highest precision score of 0.797 on the DTB70 tracking benchmark with the deformation attribute. On the UAVDT tracking benchmark with the large occlusion attribute, it achieves the highest AUC of 0.407 and the highest precision score of 0.582.

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“…For the occlusion, Ref. 13 studied a tracking algorithm in a spatiotemporal context (STC) framework, where the occlusion is detected from Average Peak to Correlation Energy (APCE)-based mechanism of response map between consecutive frames. And Ref.…”

Section: Introductionmentioning

confidence: 99%

Space dynamic target tracking method based on five-frame difference and Deepsort

Huang,

Zeng,

Xiong

et al. 2024

Sci Rep

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For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.

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Efficient Online Object Tracking Scheme for Challenging Scenarios

Cited by 10 publications

References 62 publications

A Survey of Object Detection for UAVs Based on Deep Learning

A Survey of Object Detection for UAVs Based on Deep Learning

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

Space dynamic target tracking method based on five-frame difference and Deepsort

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