A Three-Dimensional Target Recognition Algorithm Utilizing Spatiotemporal Information Perception

Abstract In order to solve the low accuracy of three-dimensional target recognition by traditional algorithms, an improved three-dimensional target recognition model based on spatiotemporal convolutional neural network is established in this paper. The high-level feature presentation ability of deep neural network and the spatiotemporal perception performance of sequential modelling methods are combined perfectly in this model. Triplet loss function is also introduced to improve the representation of three-dimensional moving targets under different attitudes, which effectively reduces the feature distance of the same targets under various attitudes, and increases the feature distance of different targets at the same time. The performance of different feature extractors and aggregation algorithms is compared in this paper. Experimental results demonstrated that the ResNet is more suitable for feature extraction and the local soft attention is more effective in feature aggregation. Compared with the traditional three-dimensional target recognition methods, the spatiotemporal perception model proposed in this paper can achieve higher recognition accuracy for three-dimensional target recognition. At the same time, the calculation speed of the model is fast, which is conducive for its application.