Zebrafish behavioral patterns reveal valuable insights for biomedical research. To accurately identify these patterns, visual tracking systems need to reconstruct 3D trajectories from multi-view video sequences. However, 3D zebrafish tracking faces challenges such as the dynamics in movements, the similarity in appearances, and the distortion caused by different viewpoints. In this paper, we propose a new method for robust 3D zebrafish trajectory reconstruction based on multi-view data fusion and global association. Our method generates reliable segments of 2D/3D trajectories, called tracklets , where we considering short-term cues of appearance similarity and motion consistency and proposing corresponding scoring metrics. Moreover, we use a lazy-reconstruction strategy to enhance the overall accuracy of 3D trajectories by taking into account the global context. Extensive experiments on the public 3D-ZeF20 dataset demonstrate the effectiveness of the proposed method, achieving 67.9% MOTA, 64.3% IDF1, and 55.0 MTBFm.

Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding.

Light field (LF) images taken by plenoptic cameras can record spatial and angular information from real-world scenes, and it is beneficial to fully integrate these two pieces of information to improve image super-resolution (SR). However, most of the existing approaches to LF image SR cannot fully fuse the information at the spatial and angular levels. Moreover, the performance of SR is hindered by the ability to incorporate distinctive information from different views and extract informative features from each view. To solve these core issues, we propose a fusion and allocation network (LF-FANet) for LF image SR. Specifically, we have designed an angular fusion operator (AFO) to fuse distinctive features among different views, and a spatial fusion operator (SFO) to extract deep representation features for each view. Following these two operators, we further propose a fusion and allocation strategy to incorporate and propagate the fusion features. In the fusion stage, the interaction information fusion block (IIFB) can fully supplement distinctive and informative features among all views. For the allocation stage, the fusion output features are allocated to the next AFO and SFO for further distilling the valid information. Experimental results on both synthetic and real-world datasets demonstrate that our method has achieved the same performance as state-of-the-art methods. Moreover, our method can preserve the parallax structure of LF and generate faithful details of LF images.

Visual pedestrian tracking represents a promising research field, with extensive applications in intelligent surveillance, behavior analysis, and human-computer interaction. However, real-world applications face significant occlusion challenges. When multiple pedestrians interact or overlap, the loss of target features severely compromises the tracker's ability to maintain stable trajectories. Traditional tracking methods, which typically rely on full-body bounding box features extracted from {Re-ID} models and linear constant-velocity motion assumptions, often struggle in severe occlusion scenarios. To address these limitations, this work proposes an enhanced tracking framework that leverages richer feature representations and a more robust motion model. Specifically, the proposed method incorporates detection features from both the regression and classification branches of an object detector, embedding spatial and positional information directly into the feature representations. To further mitigate occlusion challenges, a head keypoint detection model is introduced, as the head is less prone to occlusion compared to the full body. In terms of motion modeling, we propose an iterative Kalman filtering approach designed to align with modern detector assumptions, integrating 3D priors to better complete motion trajectories in complex scenes. By combining these advancements in appearance and motion modeling, the proposed method offers a more robust solution for multi-object tracking in crowded environments where occlusions are prevalent.