# See It Before You Grab It: Deep Learning-based Action Anticipation in Basketball

arXiv: https://arxiv.org/abs/2512.15386 · HTML: https://mlsanity.com/paper/2512.15386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 53 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://mlsanity.com/paper/2303.07347/) (2023)
- [Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video](https://mlsanity.com/paper/2005.02190/) (2020)
- [Long Short-Term Transformer for Online Action Detection](https://mlsanity.com/paper/2107.03377/) (2021)
- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [Online Action Detection](https://mlsanity.com/paper/1604.06506/) (2016)
- [Future Transformer for Long-term Action Anticipation](https://mlsanity.com/paper/2205.14022/) (2022)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data](https://mlsanity.com/paper/1502.06648/) (2015)
- [Long-Term Anticipation of Activities with Cycle Consistency](https://mlsanity.com/paper/2009.01142/) (2020)
- [Intention-Conditioned Long-Term Human Egocentric Action Forecasting](https://mlsanity.com/paper/2207.12080/) (2022)
- [T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2404.05392/) (2024)

## Abstract (arXiv)

Computer vision and video understanding have transformed sports analytics by enabling large-scale, automated analysis of game dynamics from broadcast footage. Despite significant advances in player and ball tracking, pose estimation, action localization, and automatic foul recognition, anticipating actions before they occur in sports videos has received comparatively little attention. This work introduces the task of action anticipation in basketball broadcast videos, focusing on predicting which team will gain possession of the ball following a shot attempt. To benchmark this task, a new self-curated dataset comprising 100,000 basketball video clips, over 300 hours of footage, and more than 2,000 manually annotated rebound events is presented. Comprehensive baseline results are reported using state-of-the-art action anticipation methods, representing the first application of deep learning techniques to basketball rebound prediction. Additionally, two complementary tasks, rebound classification and rebound spotting, are explored, demonstrating that this dataset supports a wide range of video understanding applications in basketball, for which no comparable datasets currently exist. Experimental results highlight both the feasibility and inherent challenges of anticipating rebounds, providing valuable insights into predictive modeling for dynamic multi-agent sports scenarios. By forecasting team possession before rebounds occur, this work enables applications in real-time automated broadcasting and post-game analysis tools to support decision-making.