Activity Detection
67 papers with code • 1 benchmarks • 12 datasets
Detecting activities in extended videos.
Benchmarks
These leaderboards are used to track progress in Activity Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Activity Detection models and implementationsDatasets
AVA
Toyota Smarthome Dataset
ROAD
TSU
Home Action Genome
MLB-YouTube Dataset
Most implemented papers
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
imatge-upc/activitynet-2016-cvprw
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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.
An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
mindorii/kws
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We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection.
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
We address the problem of activity detection in continuous, untrimmed video streams.
Fine-grained Activity Recognition in Baseball Videos
In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection.
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
In the end, a posteriori SNR weighted energy difference is applied to the extended pitch segments of the denoised speech signal for detecting voice activity.
pyannote.audio: neural building blocks for speaker diarization
We introduce pyannote. audio, an open-source toolkit written in Python for speaker diarization.
ROAD: The ROad event Awareness Dataset for Autonomous Driving
We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving.
Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization
End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once.
Learning Latent Super-Events to Detect Multiple Activities in Videos
piergiaj/super-events-cvpr18
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CVPR 2018
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.
Personal VAD: Speaker-Conditioned Voice Activity Detection
In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level.
