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Activity Detection

16 papers with code · Computer Vision

Detecting activities in extended videos.

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Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

29 Aug 2016imatge-upc/activitynet-2016-cvprw

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.

ACTION DETECTION ACTIVITY DETECTION

Learning Latent Super-Events to Detect Multiple Activities in Videos

CVPR 2018 piergiaj/super-events-cvpr18

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.

ACTION DETECTION ACTIVITY DETECTION

Temporal Gaussian Mixture Layer for Videos

ICLR 2019 piergiaj/tgm-icml19

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.

ACTION DETECTION ACTIVITY DETECTION

Fine-grained Activity Recognition in Baseball Videos

9 Apr 2018piergiaj/mlb-youtube

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection.

ACTION DETECTION ACTIVITY DETECTION ACTIVITY RECOGNITION VIDEO CLASSIFICATION

Unstructured Human Activity Detection from RGBD Images

2012 IEEE International Conference on Robotics and Automation 2012 jysung/activity_detection

Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics.

ACTION DETECTION ACTIVITY DETECTION

A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection

IEEE Access 2018 SIP-Lab/CNN-VAD

This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network.

ACTION DETECTION ACTIVITY DETECTION

Structure-Aware Convolutional Neural Networks

NeurIPS 2018 vector-1127/SACNNs

Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.

ACTIVITY DETECTION IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION TEXT CATEGORIZATION