Unlocking Video AI: PaddleVideo’s Open‑Source Solutions for Sports, Media, and Safety

Video understanding, powered by AI, is becoming essential across short‑video platforms, sports analysis, safety monitoring, and content creation, enabling automated tagging, highlight extraction, motion analysis, and real‑time violation detection.

Overview of PaddleVideo

PaddleVideo is Baidu's industry‑grade, open‑source deep‑learning platform for video tasks, offering a collection of models, algorithms, and case studies. Recent upgrades include:

Release of 10 industry‑level video application cases covering sports, internet, healthcare, media, and security.

Open‑source of five champion or top‑conference algorithms for video‑text learning, video segmentation, depth estimation, video‑text retrieval, and action recognition.

Comprehensive documentation, tutorials, live courses, and community forums for direct interaction with Baidu senior engineers.

Key Application Scenarios

1. Sports Action Recognition

FootballAction combines the PP‑TSM behavior‑recognition model, BMN temporal‑localization model, and AttentionLSTM sequence model to identify eight action types (background, goal, corner, free‑kick, yellow card, red card, substitution, out‑of‑bounds) with over 90% accuracy.

BasketballAction follows a similar framework, covering seven actions (background, three‑point, two‑point, dunk, free‑throw, jump‑ball) and also exceeds 90% accuracy.

In table‑tennis, a large‑scale dataset (>500 GB) with eight action categories (serve, forehand, short‑push, etc.) was built; start‑to‑end round detection reaches >97% accuracy and overall action recognition exceeds 80%.

Figure‑skating recognition uses pose estimation to extract joint data, feeding a ST‑GCN model to classify 30 actions, achieving a 12‑point gain over the baseline in a competition involving 300 universities and 200 companies.

2. Multimodal Video Tagging

VideoTag provides 3,000 industry‑derived tags with strong generalization, suitable for large‑scale short‑video classification, achieving 89% tag accuracy.

MultimodalVideoTag fuses visual, audio, and textual modalities, offering 25 top‑level and over 200 fine‑grained tags, with tag accuracy above 85%.

3. Intelligent Video Production

The PP‑TSM‑based video‑quality analysis model supports two production scenarios: news video clipping (providing essential footage for broadcasting) and smart cover generation (boosting click‑through rates in live‑stream and entertainment domains).

4. Interactive Video Segmentation

Based on MA‑Net, the interactive VOS tool requires only a few manually annotated frames, iteratively refines segmentation through user‑video interaction, and achieves state‑of‑the‑art performance on the DAVIS‑2017 benchmark.

5. General Action Recognition

A unified spatio‑temporal action detection model recognizes 87 classes, including 80 AVA actions and seven abnormal behaviors (e.g., swinging a stick, fighting, kicking objects, chasing, arguing, fast running, falling), outperforming traditional detection‑only pipelines.

6. Drone Detection

An open‑source drone detection model tackles challenges such as tiny targets, variable speed, and occlusion, enabling reliable detection in complex environments.

7. Medical Imaging Classification

Using public 3D‑MRI brain datasets (Neurocon, TAOWU, PPMI, OASIS‑1) covering 378 Parkinson’s disease and control cases, PaddleVideo supplies 2D/3D baseline models and four advanced classifiers. PP‑TSN and PP‑TSM achieve >91% accuracy and >97.5% AUC, while TimeSformer reaches a peak accuracy of 92.3%.

All source code, pretrained models, and documentation are hosted at https://github.com/PaddlePaddle/PaddleVideo.