Action Spotting
16 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Action Spotting
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Libraries
Use these libraries to find Action Spotting models and implementations
SoccerNet-v2
GolfDB
Latest papers with no code
Towards Active Learning for Action Spotting in Association Football Videos
In this paper, we propose an active learning framework that selects the most informative video samples to be annotated next, thus drastically reducing the annotation effort and accelerating the training of action spotting models to reach the highest accuracy at a faster pace.
Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network
In this paper, we propose a unified network for TAD, termed Faster-TAD, by re-purposing a Faster-RCNN like architecture.
Spotting Football Events Using Two-Stream Convolutional Neural Network and Dilated Recurrent Neural Network
This paper addresses the problem of event detection and localization in long football (soccer) videos.
Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting
Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games.
RMS-Net: Regression and Masking for Soccer Event Spotting
The recently proposed action spotting task consists in finding the exact timestamp in which an event occurs.
Real-Time Detection of Events in Soccer Videosusing 3D Convolutional Neural Networks
The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted.
Improved Soccer Action Spotting using both Audio and Video Streams
Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are.
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos
In this work, we propose to directly compute this ordered list by sparsely browsing the video and selecting one frame per action instance, task known as action spotting in literature.
Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions
Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset.
Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking
To extract their internal dynamics, we devised a novel Two-Phase Decomposition (TP-Decomp) of a tensor that generates very compact and discriminative representations that are robust to even heavily perturbed data.
