Advanced Correlation Filter Research Articles

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

BSCF: Learning background suppressed correlation filter tracker for wireless multimedia sensor networks

Recently, the Internet of Things (IoT) devices have been widely used in all sectors of society, especially in the field of wireless multimedia sensor networks (WMSNs). However, massive IoT video data requires efficient back-end processing to detect and track prominent objects for performing interconnection feedback. Correlation filter (CF)-based trackers present favorable properties for our back-end processing needs, such as fast computation speed, robustness to photometric and geometric variations. In this paper, we propose a novel background suppressed correlation filter (BSCF)-based target tracking method for wireless multimedia sensor networks, this method can incorporate all global background patches to enhance the tracking performance. Firstly, we reformulate the original ridge regression objective by introducing a convolution suppression term so that all real background patches will limit the generation of the filter through the circular shift operator and cropping operator. We then provide the closed form solutions of BSCF for multi-channel features via the alternating direction method of multipliers (ADMM). Further, we suggest a decomposition strategy to apply the background suppression framework as a general module to other CF-based trackers. The extensive experiments demonstrate that the proposed method performs favorably on multiple datasets against recent state-of-the-art methods. In particular, our BSCF ranks the first place on OTB-2013 with an AUC of 0.688 and an average precision of 0.904, exceeding the advanced CF tracker STRCF.

Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters

Hyperspectral imaging holds enormous potential to improve the state of the art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multimodal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this paper, we apply popular concepts from traditional object tracking, namely, kernelized correlation filters (KCFs) and deep convolutional neural network features to aerial tracking in the hyperspectral domain. We propose the deep hyperspectral KCF-based tracker (DeepHKCF) to efficiently track aerial vehicles using an adaptive multimodal hyperspectral sensor. We address low temporal resolution by designing a single KCF-in-multiple regions-of-interest (ROIs) approach to cover a reasonably large area. To increase the speed of deep convolutional features extraction from multiple ROIs, we design an effective ROI mapping strategy. The proposed tracker also provides flexibility to couple with the more advanced correlation filter trackers. The DeepHKCF tracker performs exceptionally well with deep features set up in a synthetic hyperspectral video generated by the digital imaging and remote sensing image generation (DIRSIG) software. In addition, we generate a large, synthetic, single-channel data set using DIRSIG to perform vehicle classification in the wide-area motion imagery (WAMI) platform. This way, the high fidelity of the DIRSIG software is proven, and a large-scale aerial vehicle classification data set is released to support studies on vehicle detection and tracking in the WAMI platform.

Advanced in-plane rotation-invariant correlation filters

Advanced correlation filter synthesis algorithms to achieve rotation invariance are described. We use a specified form for the filter as the rotation invariance constraint and derive a general closed-form solution for a multiclass rotation-invariant filter that can recognize a number of different objects. By requiring the filter to minimize the average correlation plane energy, we produce a multiclass rotation invariant (RI) RI-MACE filter, which controls correlation plane sidelobes and improves discrimination against false targets. To improve noise performance, we require the filter to minimize a weighted sum of correlation plane signal and noise energy. Initial test results of all filters are provided. >