Dynamicity is a critical and highly challenging aspect in Multi-Object Tracking and Segmentation (MOTS), significantly impeding the effective integration of diverse association cues. High dynamicity, such as severe occlusion or deformation, can distort appearance cues, leading to inaccurate inter-object relationships and misleading results. Conversely, in low dynamicity states, spatiotemporal consistency of appearance cues aids in recovering object states. To address this issue, we propose a straightforward, effective, and versatile Dynamicity Adaptation for Multi-object Tracking and Segmentation, named DA-Track. First, we leverage the sensitivity of appearance cues to dynamicity through pre-association, capturing dynamic behavior in objects. Second, Dynamicity Adaptation incorporates Dynamicity Selection to identify reliable appearance cues based on pre-association results and Occlusion Dynamicity Fusing to adaptively integrate appearance and motion cues by analyzing historical mask variations. Experiments on MOTS20 and KITTI MOTS datasets demonstrate DA-Track’s robust and reliable performance across diverse scenarios.

To increase the sales of agricultural products in e-commerce, understanding customer preferences is essential. In agricultural web applications, data mining techniques can help farmers analyze customer behavior patterns and identify preferences, thus optimizing product design or offering more precise personalized services, which, in turn, can enhance farmers’ decision-making in agricultural production. This study proposes a web application user behavior prediction method based on deep forest, which addresses the issue of traditional learning methods requiring a large number of hyperparameter settings. Analysis results show that the Mondrian deep forest model has an accuracy of 95.42% and a running time of 55 s. The accuracy and efficiency of the Mondrian deep forest model are higher than for other models, and the proposed model can improve the accuracy of predicting user behavior in web applications. The effectiveness of the algorithm has been validated through practical testing.