# Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1709.01421 · HTML: https://mlsanity.com/paper/1709.01421/

## Key facts

- Cited by 12 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 12 of 12)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Open Set Action Recognition via Multi-Label Evidential Learning](https://mlsanity.com/paper/2303.12698/) (2023)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [On the Fairness of Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/2103.00950/) (2021)
- [Actor-agnostic Multi-label Action Recognition with Multi-modal Query](https://mlsanity.com/paper/2307.10763/) (2023)
- [Domain Generalization using Ensemble Learning](https://mlsanity.com/paper/2103.10257/) (2021)
- [Real-time Aerial Detection and Reasoning on Embedded-UAVs](https://mlsanity.com/paper/2305.12414/) (2023)
- [Classification of Tennis Actions Using Deep Learning](https://mlsanity.com/paper/2402.02545/) (2024)
- [Deep Learning Models in Software Requirements Engineering](https://mlsanity.com/paper/2105.07771/) (2021)
- [Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers](https://mlsanity.com/paper/2104.01577/) (2021)
- [Analyzing the effectiveness of image augmentations for face recognition from limited data](https://mlsanity.com/paper/2105.08796/) (2021)
- [What augmentations are sensitive to hyper-parameters and why?](https://mlsanity.com/paper/2111.03861/) (2021)

## Abstract (arXiv)

Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of k binary networks vs. a single k-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.