In response to the problem of insufficient spatiotemporal feature extraction and difficulty in adapting to multi-source heterogeneous data in complex scenes of existing video behavior recognition models, this paper constructs a highly robust intelligent recognition and analysis system that integrates big data processing and deep learning. The core architecture employs a cooperative learning algorithm, comprising a hierarchical spatiotemporal adaptation network and a hybrid knowledge distillation (KD) mechanism. The network first identifies local and global video features via an adaptation layer, then enhances them through second-order pooling. Hybrid KD uses a teacher-student model to integrate previous human knowledge and distill it into a lightweight model that can efficiently process massive streaming data. The comparison results show that the recognition accuracy of the proposed system on the UCF101 and HMDB51 (Human Motion Database) datasets is 98.5% and 89.2%, respectively, which are 2.3% and 3.1% higher than the optimal baseline, respectively. This demonstrates the effectiveness of the framework in achieving accuracy and resource efficiency in practical video analysis systems.
Xu Zhao (Thu,) studied this question.