In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian’s entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians’ head movement tracklets, and the pedestrians’ whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.

In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language validation module is designed to use language information as the main information to increase the model’s interactivity. In addition, we also list the future possibilities of visual localization and provide two examples to explore the application and optimization direction of visual localization and human–machine interaction technology in practical scenarios, providing reference and guidance for relevant researchers and promoting the development and application of visual localization and human–machine interaction technology.

In football match videos, team affiliation is typically identified using unsupervised methods, which distinguish individuals based on unique features. These methods reduce the effort needed for dataset labeling compared to supervised approaches. However, uneven lighting in outdoor football scenes often compromises accuracy. This paper introduces a clustering method leveraging color segmentation combined with illumination equalization to address issues such as large shadows and unknown uniform designs. This method distributes personnel information—distinguishing team A, team B, goalkeepers, and referees—relying solely on color features to achieve precise clustering. Compared to established unsupervised methods, our approach demonstrated superior performance on benchmarks including the Sn-gamestate and Soccernet-Tracing datasets, which contain 81, 000 images. Additionally, we developed a shadow correction and color enhancement technique tailored for unevenly lit football scenes. Experimental results show that this method significantly improves clustering accuracy in challenging lighting conditions, boosting the Adjusted Rand Index (ARI) by at least 0.2 and enhancing color restoration markedly.