Detecting activities in extended videos.
This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
In this paper, we introduce the concept of learning latent super-events from activity videos, and present how it benefits activity detection in continuous videos.
Ranked #2 on Action Detection on Multi-THUMOS
We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos.
Ranked #1 on Action Detection on Multi-THUMOS
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics.
This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network.