Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion.

Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (${\mathrm{smooth}}_{\mathrm{RE}}$) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains.