A multi-object tracker using dynamic Bayesian networks and a residual neural network based similarity estimator

Saada, Mohamad; Kouppas, Christos; Li, Baihua; Meng, Qinggang

doi:10.1016/j.cviu.2022.103569

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…In this way, we introduce a similarity criteria between all pairs of objects (predicted and detected) calculated using the cosine similarity measure. 55 The cosine similarity measure is a method to determine how similar two vectors are. From a mathematical perspective a cosine similarity function computes the cosine of the angle between two vectors in a multi-dimensional space, The cosine similarity d 2 (•,•) is at its most when the angle is closer to zero.…”

Section: Joint Tracking and Classificationmentioning

confidence: 99%

A comparative study of joint video tracking and classification for countering unmanned aerial vehicles

Pannetier,

Vaudaux-Ruth,

Marie

et al. 2023

Automatic Target Recognition XXXIII

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The rapid adoption of autonomous Unmanned Aerial Vehicles (UAVs) for various real-world applications in both industry and the military is driving the need for efficient UAV surveillance and countering systems, as these vehicles create new threats to the safety of people and assets. These systems typically contain a variety of sensors and effectors, including video sensors that are used for both human confirmation of a potential menacing UAV, and visual servicing of effectors used to counter an aerial threat. In this case, the performance of the system depends on the accuracy of the algorithm chain (classification, l ocalization a nd t hreat i dentification) us ed for video tracking. In this paper, we study an original approach for temporally stable video tracking of targets. Specifically, w e u se s tate-of-the-art a lgorithms f or s emantic o bject d etection a nd t hen c onsolidate t hem w ith a pose estimation method to enhance the perception performance. This paper compares different a pproaches on real data.

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Section: Joint Tracking and Classificationmentioning

confidence: 99%

A comparative study of joint video tracking and classification for countering unmanned aerial vehicles

Pannetier,

Vaudaux-Ruth,

Marie

et al. 2023

Automatic Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…In order to solve these limitations, researchers have proposed some methods based on deep learning. With the rapid development of artificial intelligence, deep convolutional neural networks (CNNs) have become a dominant direction in various fields, including image inpainting [16], object tracking [17], image segmentation [18], and so on. In recent years, there is a two-stream structure based on CNN, called the Siamese network [19], which can also be viewed as a matching problem.…”

Section: Introductionmentioning

confidence: 99%

Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment

Liu

Guo

et al. 2023

Sensors

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Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to external environmental interference such as illuminance and noise, which leads to poor detection rates and poor practicality. This research develops a printing defect detection method based on scale-adaptive template matching and image alignment. Firstly, the research introduces a convolutional neural network (CNN) to adaptively extract deep feature vectors from templates and target images at a low-resolution version. Then, a feature map cross-correlation (FMCC) matching metric is proposed to measure the similarity of the feature map between the templates and target images, and the matching position is achieved by a proposed location refinement method. Finally, the matching image and the template are both sent to the image alignment module, so as to detect printing defects. The experimental results show that the accuracy of the proposed method reaches 93.62%, which can quickly and accurately find the location of the defect. Simultaneously, it is also proven that our method achieves state-of-the-art defect detection performance with strong real-time detection and anti-interference capabilities.

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“…However, deploying and inferring larger networks on mobile devices with limited memory and computational resources is challenging. To address this issue, one-stage detectors like YOLOv5 [10] have been developed with low computation requirements and high inferring speed, making them an excellent option for real-time applications, for instance, autonomous driving on mobile platforms.…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic sign detection network based on Swin Transformer

Zhu,

Ying,

zheng

et al. 2023

Preprint

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In the field of autonomous driving, the detection of traffic signs remains a significant challenge, especially when it comes to the real-time detection of medium and small targets. The difficulty of detecting small objects decreases accuracy. To address these challenges, we propose a real-time traffic sign detection algorithm based on the Swin Transformer (RTSDST) that improves computation performance and accuracy for multi-scale target detection on SoCs installed onboard autonomous driving vehicles. Our approach includes a head specifically designed for detecting tiny objects, followed by the adoption of Swin Transformer blocks to effectively capture the spatial and channel dependencies of the feature maps, which improves the accuracy of detecting targets of varying sizes. To efficiently identify regions of interest in large coverage images, we employ a Residual Convolutional Attention Module to generate sequential feature maps between the channel and spatial dimensions and weigh them against the original map. A realistic traffic sign detection dataset, Tsinghua-Tencent 100K (TT100K), which includes medium and small traffic sign targets, was adopted in this article to evaluate the effectiveness of our proposed RTSDST. The evaluation results show that RTSDST has excellent performance on multi-scale scenes. Additionally, we also evaluated our network on the VisDrone dataset for small target detection. Our method has state-of-art performance on small targets.

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A multi-object tracker using dynamic Bayesian networks and a residual neural network based similarity estimator

Cited by 4 publications

References 24 publications

A comparative study of joint video tracking and classification for countering unmanned aerial vehicles

A comparative study of joint video tracking and classification for countering unmanned aerial vehicles

Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment

Real-time traffic sign detection network based on Swin Transformer

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