A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Su, Yinqiang; Liu, Jinghong; Xu, Fang; Zhang, Xueming; Zuo, Yujia

doi:10.3390/rs13224672

Cited by 7 publications

(4 citation statements)

References 53 publications

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“…where ReLu means the activation function, 𝑊 and 𝑏 (i = 1, 2) represent para weight and biases. Taking 𝐿 as an example, we use 𝐿 as query, 𝐿 as the ke value, then we use formulation (5) to calculate the global information interrelati tween 𝐿 and 𝐿 . The result is added to 𝐿 as residual connection, then we apply normalization and feed-forward and layer-normalization again to establish inform interaction between 𝐿 and 𝐿 .…”

Section: Transformer-based Multi-scale and Global Feature Encodermentioning

confidence: 99%

“…where ReLu means the activation function, W i and b i (I = 1, 2) represent parameter weight and biases. Taking L 1 as an example, we use L 2 as query, L 1 as the key and value, then we use formulation (5) to calculate the global information interrelation between L 2 and L 1 .…”

Section: Prediction Head Attention Modulementioning

confidence: 99%

“…However, in aerial remote sensing tracking, there are many challenges, such as scale variation, viewpoint change, occlusion, similar object and background clutter. These challenges make accurate and robust aerial tracking still a challenging task [5].…”

Section: Introductionmentioning

confidence: 99%

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Global Multi-Scale Optimization and Prediction Head Attentional Siamese Network for Aerial Tracking

Chen,

Liu,

Wang

et al. 2023

Symmetry

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Siamese-based trackers have been widely used in object tracking. However, aerial remote tracking suffers from various challenges such as scale variation, viewpoint change, background clutter and occlusion, while most existing Siamese trackers are limited to single-scale and local features, making it difficult to achieve accurate aerial tracking. We propose the global multi-scale optimization and prediction head attentional Siamese network to solve this problem and improve aerial tracking performance. Firstly, a transformer-based multi-scale and global feature encoder (TMGFE) is proposed to obtain global multi-scale optimization of features. Then, the prediction head attentional module (PHAM) is proposed to add context information to the prediction head by adaptively adjusting the spatial position and channel contribution of the response map. Benefiting from these two components, the proposed tracker solves these challenges of aerial remote sensing tracking to some extent and improves tracking performance. Additionally, we conduct ablation experiments on aerial tracking benchmarks, including UAV123, UAV20L, UAV123@10fps and DTB70, to verify the effectiveness of the proposed network. The comparisons of our tracker with several state-of-the-art (SOTA) trackers are also conducted on four benchmarks to verify its superior performance. It runs at 40.8 fps on the GPU RTX3060ti.

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Section: Transformer-based Multi-scale and Global Feature Encodermentioning

confidence: 99%

Section: Prediction Head Attention Modulementioning

confidence: 99%

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Global Multi-Scale Optimization and Prediction Head Attentional Siamese Network for Aerial Tracking

Chen,

Liu,

Wang

et al. 2023

Symmetry

Self Cite

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“…Correctly distinguishing the foreground from the background is the key factor to locate the target object location and generate the predicted banding box. A step further, according to the implementation principle, it can be divided into correlation filter-based [14][15][16][17] and deep learning-based [18][19][20][21] tracking models. The key step in the correlation filter approach is to obtain the filter with optimal performance so that the search area and the target area could generate an accurate maximum response.…”

Section: Introductionmentioning

confidence: 99%

SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking

Liu

Chen

et al. 2023

Micromachines

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For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios.

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Multiple object tracking with behavior detection in crowded scenes using deep learning

Gullapelly

Banik

2023

IFS

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Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%.

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A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Cited by 7 publications

References 53 publications

Global Multi-Scale Optimization and Prediction Head Attentional Siamese Network for Aerial Tracking

Global Multi-Scale Optimization and Prediction Head Attentional Siamese Network for Aerial Tracking

SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking

Multiple object tracking with behavior detection in crowded scenes using deep learning

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