The perception of drones, also known as Unmanned Aerial Vehicles (UAVs), particularly in infrared videos, is crucial for effective anti-UAV tasks. However, existing datasets UAV tracking have limitations terms target size and attribute distribution characteristics, which do not fully represent complex realistic scenes. To address this issue, we introduce a generalized benchmark called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Robust 3D perception amidst corruption is a crucial task in the realm of vision. Conventional data augmentation methods aimed at enhancing robustness typically apply random transformations to all point cloud samples offline, neglecting sample structure, which often leads over- or under-enhancement. In this study, we propose an alternative approach address issue by employing sample-adaptive based on through auto-augmentation framework named AdaptPoint++. Central imitator, initiates with...

With the growing threat of unmanned aerial vehicle (UAV) intrusion, anti-UAV techniques are becoming increasingly demanding. Object tracking, especially in thermal infrared (TIR) videos, though provides a promising solution, struggles with challenges like small scale and fast movement that commonly occur scenarios. To mitigate this, we propose simple yet effective spatio-temporal attention based Siamese network, dubbed SiamSTA, to track UAV robustly by performing reliable local tracking...

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking drone targets difficult challenging. This work proposes simple effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching wide-range re-detection...

Unmanned aerial vehicle (UAV) tracking focus on moving targets from flying platforms, where the target undergoes a lot of aspect ratio changes, <i>i.e</i>., viewpoint change, rotation. Discriminative correlation filter (DCF) based method shows promising solution to UAV due its high computational efficiency. DCF trackers apply fixed-bandwidth Gaussian function label for model training and incremental update adapt dramatic appearance changes in during tracking. However, poor regression ability...

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage their complementary strength. In addition, online update strategies are limited using a single fixed learning rate, which often leads model degradation when suffering challenges. this paper, we present an Auto-Perceiving...

With the growing threat of unmanned aerial vehicle (UAV) intrusions, topic anti-UAV tracking has received widespread attention from community. Traditional Siamese trackers struggle with small UAV targets and are plagued by model degradation issues. To mitigate this, we propose a novel Searching Region-free Template-free network (SiamSRT) to track in thermal infrared (TIR) videos. The proposed tracker builds two-stage architecture former providing detection first-frame groundtruth using...

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A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in have no obvious contour are susceptible to the influence environments, equipment noises, other factors, resulting varied shapes sizes. Compared with traditional detection methods, this paper proposes improved YOLOv7 (You Only Look Once vision 7) network structure for detecting a WCI. Firstly, Fused-MBConv used replace all...

We present iARVis, a proof-of-concept toolkit for creating, experiencing, and sharing mobile AR-based information visualization environments. Over the past years, AR has emerged as promising medium data beyond physical media desktop, enabling interactivity eliminating spatial limits. However, creation of such environments remains difficult frequently necessitates low-level programming expertise lengthy hand encodings. declarative approach defining augmented reality (AR) environment,...

The propagation of flight delays is challenging to analyze because delay events depend on multiple variables. This phenomenon has become even worse with the increasing number aircraft in China, and research into shown limited progress. In this paper, we design a visual analysis system for propagation. Unlike conventional research, work focuses trends one region representing relationship occurring airports. First, construct Bayesian network parameters select factors visualization. Second,...

The tracking performance of discriminative correlation filters (DCFs) is often subject to unwanted boundary effects. Many attempts have already been made address the above issue by enlarging searching regions over last years. However, introducing excessive background information makes filter prone learn from surrounding context rather than target. In this article, we propose a novel restrained (CRCTF) that can effectively suppress interference via incorporating high-quality adversarial...

Semantic segmentation plays an important role in widespread applications such as autonomous driving and robotic sensing. Traditional methods mostly use RGB images which are heavily affected by lighting conditions, \eg, darkness. Recent studies show thermal robust to the night scenario a compensating modality for segmentation. However, existing works either simply fuse RGB-Thermal (RGB-T) or adopt encoder with same structure both stream stream, neglects difference under varying conditions....

In a tampered blurred image generated by splicing, the spliced region and original may have different blur types. Splicing detection in this is challenging problem. recent years, researchers proposed various methods for detecting such splicing. paper, we propose novel framework splicing based on partial type inconsistency. framework, after cepstrum-based transforming, classification parameter extracted from spectrum characteristics of image. The restored kernel which constructed estimating...

Traditional discriminative correlation filter (DCF) tracking algorithms always use ideal Gaussian functions as labels to train filters, which has reached promising performance in usual scenarios. However, due challenges such camera motion, occlusion and similar targets appearing frequently unmanned aerial vehicle (UAV) scenarios, these trackers using the identical fixed often lead over-fitting model degradation therefore perform poorly UAV tracking. Accordingly, we present a new framework...

Most existing tracking methods based on discriminative correlation filters (DCFs) update the tracker every frame with a fixed learning rate. However, constantly adjusting can hardly handle fickle target appearance in UAV (e.g., undergoing partial occlusion, illumination variation, or deformation). To mitigate this, we propose novel auto-learning filter for tracking, which fully exploits valuable information behind response maps adaptive feedback updating. Concretely, first introduce...

Most of the existing Siamese-based trackers treat tracking problem as a parallel task classification and regression. However, some studies show that sibling head structure could lead to suboptimal solutions during network training. Through experiments we find that, without regression, performance be equally promising long delicately design suit training objective. We introduce novel voting-based classification-only algorithm named Pyramid Correlation based Deep Hough Voting (short for...

Deep learning has been widely adopted to tackle various code-based tasks by building deep code models based on a large amount of snippets. While these have achieved great success, even state-of-the-art suffer from noise present in inputs leading erroneous predictions. it is possible enhance through retraining/fine-tuning, this not once-and-for-all approach and incurs significant overhead. In particular, techniques cannot on-the-fly improve performance (deployed) models. There are currently...