A UAV Target Prediction and Tracking Method Based on KCF and Kalman Filter Hybrid Algorithm

The on‐board target tracking of unmanned aerial vehicle (UAV) relies on on‐board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real‐time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real‐time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high‐level synthesis technology, and a high‐speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real‐time requirements of UAV target tracking task.

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively.

Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of hierarchical lightweight networks has become a prevalent strategy for enhancing efficiency in UAV tracking, it often results in a significant drop in network capacity, which further exacerbates challenges in UAV scenarios, such as frequent occlusions and extreme changes in viewing angles. To address these issues, we introduce a novel family of UAV trackers, termed CGTrack, which combines explicit and implicit techniques to expand network capacity within a coarse-to-fine framework. Specifically, we first introduce a Hierarchical Feature Cascade (HFC) module that leverages the spirit of feature reuse to increase network capacity by integrating the deep semantic cues with the rich spatial information, incurring minimal computational costs while enhancing feature representation. Based on this, we design a novel Lightweight Gated Center Head (LGCH) that utilizes gating mechanisms to decouple target-oriented coordinates from previously expanded features, which contain dense local discriminative information. Extensive experiments on three challenging UAV tracking benchmarks demonstrate that CGTrack achieves state-of-the-art performance while running fast. Code will be available at https://github.com/Nightwatch-Fox11/CGTrack.

Unmanned aerial vehicle (UAV) tracking is of great significance for a wide range of applications, such as delivery and agriculture. Previous benchmarks in this area mainly focused on small-scale tracking problems while ignoring the amounts of data, types of data modalities, diversities of target categories and scenarios, and evaluation protocols involved, greatly hiding the massive power of deep UAV tracking. In this article, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames across 4,500 videos and offers 223 highly diverse target categories. Each video is densely annotated with bounding boxes by an efficient and scalable semi-automatic target annotation (SATA) pipeline. Importantly, to take advantage of the complementary superiority of language and audio, we enrich WebUAV-3M by innovatively providing both natural language specifications and audio descriptions. We believe that such additions will greatly boost future research in terms of exploring language features and audio cues for multi-modal UAV tracking. In addition, a fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers. We provide extensive evaluations and detailed analyses of 43 representative trackers and envision future research directions in the field of deep UAV tracking and beyond. The dataset, toolkits, and baseline results are available at https://github.com/983632847/WebUAV-3M.

With the rapid development in the field of computer vision, the vision-based approach to unmanned aerial vehicle (UAV) tracking and landing technology in weak global positioning system (GPS) or GPS-free environments has become prominent in military and civilian missions. However, this technique still suffers from problems such as interference by similar targets in the environment, low tracking accuracy, slow processing speed, and poor stability. To solve these problems, we propose the designated target anti-interference tracking (DTAT) method, which integrates YOLOv5 and SiamRPN, and built a system to achieve UAV tracking and the landing of a designated target in an environment with multiple interference targets. The system consists of the following parts: first, an image is acquired by a monocular camera to obtain the pixel position information of the designated target. Next, the position of the UAV relative to the target is estimated based on the pixel location information of the target and the known target size information. Finally, the discrete proportion integration differentiation (PID) control law is used to complete the target tracking and landing task of the UAV. To test the system performance, we deployed it on a robot operating system (ROS) platform, conducted many simulation experiments, and observed the real-time trajectories of the UAV and the target through Gazebo software. The results show that the relative distance between the UAV and the target during the tracking process when the target was moving at 0.6 m/s does not exceed 0.8 m, and the landing error of the UAV during the landing process after the target is stationary does not exceed 0.01 m. The results validate the effectiveness and robustness of the system and lay a foundation for subsequent research.

Neural networks (NNs) are now being extensively utilized in various artificial intelligence platforms specifically in the area of image classification and real-time object tracking. We propose a novel design to address the problem of real-time unmanned aerial vehicle (UAV) monitoring and detection using a Zynq UltraScale FPGA-based convolutional neural network (CNN). The biggest challenge while implementing real-time algorithms on FPGAs is the limited DSP hardware resources available on FPGA platforms. Our proposed design overcomes the challenge of autonomous real-time UAV detection and tracking using a Xilinx’s Zynq UltraScale XCZU9EG system on a chip (SoC) platform. Our proposed design explores and provides a solution for overcoming the challenge of limited floating-point resources while maintaining real-time performance. The solution consists of two modules: UAV tracking module and neural network–based UAV detection module. The tracking module uses our novel background-differencing algorithm, while the UAV detection is based on a modified CNN algorithm, designed to give the maximum field-programmable gate array (FPGA) performance. These two modules are designed to complement each other and enabled simultaneously to provide an enhanced real-time UAV detection for any given video input. The proposed system has been tested on real-life flying UAVs, achieving an accuracy of 82%, running at the full frame rate of the input camera for both tracking and neural network (NN) detection, achieving similar performance than an equivalent deep learning processor unit (DPU) with UltraScale FPGA-based HD video and tracking implementation but with lower resource utilization as shown by our results.

As a significant research direction in remote sensing fields, unmanned aerial vehicles (UAVs) tracking has achieved rapid development in recent years. However, due to limited power and computation resources on aerial platforms, the tracking methods deployed on UAVs usually require high computational efficiency and performance. In addition, various challenges (i.e., similar object, background clutter and occlusion) have inevitably occurred during the UAV tracking phase. Therefore, considering the above issues comprehensively, this paper proposes a dynamic aberrance-repressed temporal regularized correlation filter to achieve stable tracking in UAV remote sensing videos. First, we have introduced the aberrance-repressed temporal regularizations into the discriminative correlation filter (DCF) framework. Second, a novel objective loss function is constructed to adjust the strength of each regularization for training the filter. Then, a new judgment mechanism based on the response variation is exploited to reflect the response fluctuation and applied to tune parameters of both regularizations. Finally, comprehensive experiments are done on three different UAV benchmarks, i.e., UAV123@10fps, UAVDT and VisDrone2018, to verify the performance of our tracker and have demonstrated that our tracker achieves superior performance against other total 25 state-of-the-art trackers while reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 35 FPS on a single CPU.

The target tracking technology in the UAV autonomous landing process is studied. Based on the analysis of the occlusion, background change, and variable scale problems in the target tracking process, a scale adaptive method for UAV target tracking based on KCF (Kernelized Correlation Filter) algorithm is proposed. The original KCF target tracking framework is adopted to achieve the target position prediction function. Secondly, to address the variable scale problem of moving targets and the target loss problem caused by scale changes, the scale filter of the DSST target tracking algorithm is used. The algorithm is for estimating the scale of moving targets and generating a small-scale filter estimate to improve the tracking efficiency of the UAV for moving targets and reduce the target loss rate. The final test on the OTB dataset showed a 4% improvement in variable scale accuracy and a 2.9% improvement in variable scale success rate. A Gazebo 3D environment scene was set up under the Linux system, and UAV autonomous landing simulation experiments were conducted to verify the improved algorithm’s real-time effectiveness in the UAV autonomous landing application. The simulation results show that the improved KCF target tracking algorithm has real-time performance in the UAV autonomous landing trajectory tracking process.

Siamese-based trackers have shown excellent performance in unmanned aerial vehicle (UAV) tracking due to their balanced accuracy and speed. However, most existing methods rely heavily on local convolution operations of CNNs for feature extraction, which fails to establish global feature relationships. In addition, the challenge of information imbalance, caused by differences in background and the prevalence of single-object scenarios, remains a significant obstacle in the current domain. This can potentially result in unwanted boundary effects in UAV tracking. To address these challenges, this article introduces a novel tracker called eccentric loss and adaptive search (ELAS), which is specifically designed for UAV videos and aims to overcome these challenges. The proposed ELAS enhances feature representation capabilities by utilizing multi-input and multimapping of information. It also addresses information imbalance through a differentiated loss function. Furthermore, an adaptive search strategy is introduced to accommodate varying search scales across different scenarios. Specifically, an additional multilayer feature enhancement (MLE) network is first introduced, which absorbs the middle and high layer information in the backbone network and directs the features to focus on global connections using self-attention, avoiding the loss of useful semantic information due to falling into local optima. Then, a novel eccentric loss is designed to guide network training, enhancing the saliency of object features on the feature map by accounting for the volatility of negative samples and estimating classification performance. Finally, an adaptive multiscale search strategy is proposed to detect the saliency of objects through between-class segmentation effectiveness and, thus, adjust the proportional scale parameter. Extensive experiments on challenging UAV benchmarks demonstrate that ELAS achieves state-of-the-art (SOTA) performance with real-time speed, confirming its effectiveness and robustness.

Unmanned Aerial Vehicles (UAV), such as quadrotors or fixed-wing aircraft, are increasingly common in airspace between 100 and 2000 meters. Extending current aviation infrastructure management operations, such as flight plan approval or aircraft tracking, to include UAV systems is difficult for multiple reasons, such as the technical challenge of tracking objects that frequently travel below the radar floor. This work explores the feasibility of using cellular data modems to achieve low-cost UAV tracking solutions for small consumer UAVs, such as *rotors, which typically have a very low-altitude flight path. We also explore the altitude at which the data modem functionality diminishes. We present a UAV tracking architecture and web service interface, as well as the empirical evidence gathered from testing the real-time capabilities and limitations of such a system.

Real-time object tracking for unmanned aerial vehicles (UAVs) is an essential and challenging research topic for computer vision. However, the scenarios that UAVs deal with are complicated, and the UAV tracking targets are small. Therefore, general trackers often fail to take full advantage of their performance in UAV scenarios. In this paper, we propose a tracking method for UAV scenes, which utilizes background cues and aberrances response suppression mechanism to track in 4 degrees-of-freedom. Firstly, we consider the tracking task as a similarity measurement problem. In this study, we decompose this problem into two subproblems for optimization. Secondly, to alleviate the problem of small targets in UAV scenes, we utilize background cues fully. Also, to reduce interference by background information, we employ an aberrance response suppression mechanism. Then, to obtain accurate target state information, we introduce a logarithmic polar coordinate system. We perform phase correlation calculations in logarithmic polar coordinates to obtain the rotation and scale changes of the target. Finally, target states are obtained through response fusion, which includes displacement, scale, and rotation angle. Our approach is carried out in a large number of experiments on various UAV datasets, such as UAV123, DBT70, and UAVDT2019. Compared with the current advanced trackers, our method is superior on UAV small target tracking.

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories.

In this paper, a novel three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-enabled passive UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS), which calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem whose goal is to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. To address this problem, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods that use feedforward neural networks to approximate Q functions, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the value decomposition based-MARL scheme with FAS and the proposed AR-MARL method without FAS.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather. Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications. This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.