Satellite video multi-object tracking (MOT) is fundamentally challenged by extremely weak inter-frame displacement. Most existing methods capture motion at the image level, limiting the effective utilization of fine-grained motion information. To address this limitation, we propose a Flow-driven Motion-Aware Network(FMA-Net) that performs pixel-level motion modeling via the designed Flow-driven Motion Estimator (FME). The captured fine-grained motion is then fully exploited by the Motion-Aware Feature Fusion (MAF) module to guide inter-frame feature fusion, allowing the network to focus on truly dynamic targets during detection. The Motion-Aware Refinement (MAR) module leverages the motion priors to maintain identity consistency and reduce mismatches during association, improving tracking robustness. By jointly modeling and exploiting fine-grained motion cues throughout both detection and association, FMA-Net significantly enhances tracking accuracy in satellite video MOT. Extensive experiments on three satellite video MOT benchmarks demonstrate that FMA-Net achieves state-of-the-art performance across diverse object categories, validating its effectiveness and generalization capability. The code will be publicly available at: https://github.com/luweiqing/FMA-Net.

The pedestrian attribute recognition aims to generate a structured description of pedestrians, which serves an important role in surveillance. Current works usually assume that the images and the specific pedestrian states, including pedestrian occlusion and pedestrian orientation, are given. However, we argue that the current works ignore the guidance of the pedestrian state and cannot achieve the appropriate performance since the appearance feature will become unreliable due to the variance of the pedestrian state, which is common in practice. Therefore, this paper proposes the Explicit State Representation (ExSR) Guided Pedestrian Attribute Recognition to improve the accuracy through state learning and attribute fusion among frames. Firstly, the pedestrian state is explicitly represented by concatenating the pedestrian orientation and occlusion, which can be accurately determined via analyzing the pose. Secondly, the state-aware pedestrian attribute fusion method is proposed and divided into two cases, namely the inter-state case and the intra-state case. In the intra-state case, the appearance feature will remain stable and the attribute relations are propagated to refine. The method of exploiting attribute relations within a single frame is the Graph Neural Network. In the inter-state case, the state changes, the attribute relationship propagation is prevented, and the advantages of attribute recognition in each frame are complemented to make a reliable judgment on the invisible region. The experimental results demonstrate that the ExSR outperforms the state-of-the-art methods on two public databases, benefiting from the explicit introduction of the state into the attribute recognition.

Pedestrian attribute recognition (PAR) aims to generate a structured description of pedestrians and plays an important role in surveillance. Current work focusing on 2D images can achieve decent performance when there is no variation in the captured pedestrian orientation. However, the performance of these works cannot be maintained in scenarios when the orientation of pedestrians is ignored. To mitigate this problem, this paper proposes orientation-aware pedestrian attribute recognition based on graph convolution network (GCN), which is composed of an orientation-aware spatial attention (OSA) module and an orientation-guided attribute-relation learning (OAL) module. Since some attributes can be invisible for certain orientations, OSA is proposed for orientation-aware feature extraction to enhance the learned representation of the visual attributes. Moreover, since different orientations result in different relations among attributes, OAL is proposed to achieve distinguishable and impactful attribute relations by eliminating the confusion of attribute relations in different orientations. Experiments on three challenging datasets (PETA, RAP, and PA100K) demonstrate that the proposed PAR outperforms the state-of-the-art methods by considerable margins.

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs.

The pedestrian attribute recognition aims at generating the structured description of pedestrian, which plays an important role in surveillance. However, it is difficult to achieve accurate recognition results due to diverse illumination, partial body occlusion and limited resolutions. Therefore, this paper proposes a comprehensive relationship framework for comprehensively describing and utilizing relations among attributes, describing different type of relations in the same dimension, and implementing complex transfers of relations in a GCN manner. This framework is named Correlation Graph Convolutional Network (CGCN). Based on the proposed framework, the feature vectors are built to associate attributes with image features and generate different relation matrices through self-attention among different feature vectors, describing different attribute relations. Then, we conduct multi-layer transfer of attribute relations by means of graph convolution, realizing complex utilization of attribute relations. In addition, the relations among attributes are fully exploited and two types of relations, namely the explicit and implicit relations, are proposed to be integrate into the proposed comprehensive relationship framework. The experimental results on RAP and PETA demonstrate that the recognition performance of the proposed CGCN can obviously outperform the state-of-the-arts, and moreover, the CGCN can achieve a better synergy with different relations.