Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

Understand

We present a deep-learning framework for real-time multiple spatio-temporal (S/T) action localisation, classification and early prediction.

• Current state-of-the-art approaches work offline and are too slow to be useful in real- world settings.
• To overcome their limitations we introduce two major developments.
• Firstly, we adopt real-time SSD (Single Shot MultiBox Detector) convolutional neural networks to regress and classify detection boxes in each video frame potentially containing an action of interest.