Multiple-Object Tracking (MOT) has been an attracting area in these years with excellent progresses. However, there are still complicated uncertainties caused by movement of pedestrians due to environmental disturbance and mental state. To handle this, we characterize pedestrian moving patterns through a dual-phase paradigm, and Probabilistic Graphical Model (PGM) is introduced into our work to build Hidden Markov Model (HMM) to better understand the uncertainties. Moreover, the weighted entropy mechanism is utilized in feature fusion for balanced importance between appearance and motion information. Finally, our method achieves state-of-the-art results on MOT17 and MOT20 datasets, especially in the category of graphical methods.

Although current ReID (Re-Identification) methods have become relatively mature, they still require manual extraction of pedestrian images and annotation of features. They lack semantic understanding capabilities in open scenes. While some ReID models integrated with LLMs (Large Language Models) offer more comprehensive functions and better performance, they still fall short in terms of semantic understanding and cross-modal retrieval. To solve these problems, we introduce SUO-ReID, a semantic understanding-based approach for ReID in open scenes. SUO-ReID combines LVLM (Large Vision-Language Model) with ResNet (Residual Network) to extract high-level semantic features of targets in open scenes. It can also perform more flexible and complex functions, such as searching for or comparing targets with specified features, through instruction inputs. Experimental results show that SUO-ReID achieves an accuracy rate of 95.73% on datasets such as Market-1501 and DukeMTMC and exhibits excellent semantic understanding capabilities in open scenes, supporting cross-modal retrieval. It can also provide a detailed description of the features of the identified object and its surrounding scene. This study provides new insights into the application of large vision-language models in the field of ReID.

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial Data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial Data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies.