Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios featuring instances of missed detections, false alarms, and frequent target switching leading to tracking failures, we propose an approach to multi-object tracking utilizing KC-YOLO detection and an identity validity discrimination module. We have constructed the KC-YOLO detection model as the detector for the tracking task, optimized the selection of detection frames, and implemented adaptive feature refinement to effectively address issues such as incomplete pedestrian features caused by occlusion. Furthermore, we have introduced an identity validity discrimination module in the data association component of the tracker. This module leverages the occlusion ratio coefficient, denoted by “k”, to assess the validity of pedestrian identities in low-scoring detection frames following cascade matching. This approach not only enhances pedestrian tracking accuracy but also ensures the integrity of pedestrian identities. In experiments on the MOT16, MOT17, and MOT20 datasets, MOTA reached 75.9%, 78.5%, and 70.1%, and IDF1 reached 74.8%, 77.8%, and 72.4%. The experimental results demonstrate the superiority of the methodology. This research outcome has potential applications in security monitoring, including public safety and fire prevention, for tracking critical targets.

The effective association of multimodal data is the basis of massive multi-source heterogeneous data sharing in the era of big data. How to realize data autonomous association between massive multimodal databases and the automatic intelligent screening of valuable information from associated data, so as to provide a reliable data source for artificial intelligence (AI), is an urgent problem to be solved. In this paper, a data autonomous association method based on the organizational structure of data cells is proposed, including transaction abstraction based on information nucleuses, symmetric and asymmetric data association based on strategies and data pipes, and information generation based on big data. To screen meaningful data associations, an information-driven intelligent information discovery method and a task-driven intelligent information discovery method are proposed. The former screens meaningful data associations by training the reward and punishment model to simulate the manual scoring of data associations. The latter is task-oriented and screens meaningful data associations by training the reward and punishment model to simulate the manual ranking of data associations related to the task requests. Through the above work, autonomous data association and intelligent information discovery are effectively realized based on multimodal fusion technology, which provides a novel data source mining approach using multimodal data sharing and intelligent information discovery.

Fast and accurate extraction of multi-scale semantic information is crucial for indoor applications such as navigation and personalized services. This paper focuses on large-scale indoor scene categories and small-scale indoor element semantics, proposing an RGB-based dual-task model for scene recognition and semantic segmentation. The model employs a pyramid pooling module to create a shared feature layer for both tasks. The scene recognition branch incorporates an SE attention mechanism and a Transformer module to enhance scene understanding, while the semantic segmentation branch fuses low-level features from ResNet50 to improve learning of shapes and textures. This paper investigates the fusion methods of scene-level and element-level features and explores the optimal training strategy for the dual-task model. It also discusses and analyzes the impact of different weight combinations of dual-task Loss values on the model’s performance. The experimental results demonstrate that the overall accuracy (OA) of scene recognition and semantic segmentation of this method is 98.4%, and the overall accuracy (OA) of semantic segmentation is 82.6%. The average time for processing a single scene is approximately 0.037 s, which is superior to the latest models compared in this paper. It is found that the optimal training strategy for a single task is conducive to enhancing the accuracy of dual tasks. Moreover, when the loss weights of the two tasks in this method are both 1, the comprehensive effect of the model is the best.

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.

Night-light remote sensing imagery (NLRSI) effectively reflects urban economic and human activities and has important value in the field of remote sensing. However, the applications are limited by their low spatial resolution. In recent years, multisource remote sensing image fusion has become an important method for enhancing the spatial and spectral resolution of single-image data. To address the low-resolution limitation of NLRSI, this study proposes a multisource remote sensing image fusion framework based on the two-branch convolutional neural network (TbCNN), which fuses Landsat-8 and NPP/VIIRS data to generate high-resolution color night-light remote sensing imagery (CNLRSI). First, TbCNN features a deep two-branch structure for multiscale feature extraction, yielding richer spatial texture features. The framework also integrates a multilevel feature fusion module and a residual learning mechanism, further improving the fusion performance of CNLRSI. Quantitative evaluations demonstrate TbCNNs superiority over other methods, achieving optimal values in objective evaluation metrics. Second, in the built-up area extraction experiment, CNLRSI better identifies the true morphology of urban built-up areas compared with NPP/VIIRS data, reducing the overestimation of central urban areas and the underestimation of suburban areas in NPP/VIIRS data. Finally, the enhanced classification capability of CNLRSI is quantitatively validated through confusion matrix analysis, achieving a higher Kappa coefficient (0.814 versus 0.774) than NPP/VIIRS in urban pixel recognition.

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.