Generally, the performance of deep learning‐based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L‐PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state‐of‐the‐art methods.

To increase the accuracy of pose estimation on multiple datasets, this paper proposes a new pose estimation method based on the fusion of multiple models. This method consists of two main steps: firstly, a dataset classification by YoloV5 algorithm is performed that is to classify the dataset that may contain the testing samples; secondly, the key point detection is performed by using a deep learning model-based pose estimation that is trained on the predicted dataset. We evaluated the methods on three publicly datasets: Leeds Sports Pose (LSP) dataset, CrowdPose dataset and AnimalPose one. As the experimental results show, our method can achieve the scalability on multiple datasets while ensuring high accuracy.