This paper proposes a method to monitor video-based pedestrian re-identification based on adaptive weight assignment using deep learning. The contribution rate of the pedestrian attribute to the classification is calculated based on the training difficulty of the pedestrian attribute reflected by verification loss along with the correspondence information entropy of the pedestrian attribute and pedestrian category. The training loss weight of the pedestrian attribute multi-task classification is adaptively solved. The negative transfer problem caused by the same loss weight is assigned to improve the generalization abilities of each task learner and pedestrian re-identification. The trained model solves the attribute probability and combines the conditional probability to discriminate the pedestrian category using the mapping relationship between the pedestrian attribute and the pedestrian category in the existing data set, which overcomes the problem that cannot identify pedestrian category because of the dramatic change of the pedestrian appearance. Based on objective and quantitative comparison with similar methods on different public data test sets, the results show that the method is effective and feasible.