PROBABILISTIC SAMPLING WITH FROBENIUS NORM FOR ACTION RECOGNITION

Efficient video human activity recognition requires selecting relevant frames or segments of consecutive frames (clip) in a video, while also minimizing computational costs. Existing sampling methods are either deterministic and straightforward, or complex and computationally expensive. In addition, some samplers often lack adaptability and fail to account for the uncertainty inherent in dynamic action sequences. To address these limitations, we present a probabilistic sampling strategy that balances adaptability and efficiency. Leveraging the Frobenius norm as a lightweight motion-change metric, our method assigns probabilistic importance scores to clips via softmax normalization and employs a stochastic sampling scheme based on the softmax scores to prioritize relevant segments. Unlike deterministic approaches, our method captures the dynamic and uncertainty of actions without the overhead of complex models. Experiments on UCF101, HMDB51 and Diving48 datasets validate that our method achieves competitive accuracy with significantly lower computational complexity.