# Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1611.02447 · HTML: https://mlsanity.com/paper/1611.02447/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2016.

## Built on by (most cited first, 30 of 32)

- [SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition](https://mlsanity.com/paper/2403.09508/) (2024)
- [SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2209.02399/) (2022)
- [Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.02693/) (2022)
- [Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2408.09764/) (2024)
- [Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.02327/) (2023)
- [Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning](https://mlsanity.com/paper/2405.20606/) (2024)
- [Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks](https://mlsanity.com/paper/2301.11495/) (2023)
- [Skeleton-Based Human Action Recognition with Noisy Labels](https://mlsanity.com/paper/2403.09975/) (2024)
- [STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition](https://mlsanity.com/paper/2303.18177/) (2023)
- [Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition](https://mlsanity.com/paper/2508.03695/) (2025)
- [A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2111.06995/) (2021)
- [Progressive Cross-modal Knowledge Distillation for Human Action Recognition](https://mlsanity.com/paper/2208.08090/) (2022)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Towards Robust and Realistic Human Pose Estimation via WiFi Signals](https://mlsanity.com/paper/2501.09411/) (2025)
- [3D Plant Root Skeleton Detection and Extraction](https://mlsanity.com/paper/2508.08094/) (2025)
- [Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2301.13360/) (2023)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [Universal Skeleton Understanding via Differentiable Rendering and MLLMs](https://mlsanity.com/paper/2603.18003/) (2026)
- [Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT](https://mlsanity.com/paper/2212.12264/) (2022)
- [Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2408.02855/) (2024)
- [Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders](https://mlsanity.com/paper/2504.09530/) (2025)
- [Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.05482/) (2021)
- [Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning](https://mlsanity.com/paper/2305.19343/) (2023)
- [Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning](https://mlsanity.com/paper/2306.17590/) (2023)
- [SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System](https://mlsanity.com/paper/2311.08094/) (2023)
- [Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art](https://mlsanity.com/paper/2311.12300/) (2023)
- [One-Shot Multi-Rate Pruning of Graph Convolutional Networks](https://mlsanity.com/paper/2312.17615/) (2023)
- [Unsupervised Motion Retargeting for Human-Robot Imitation](https://mlsanity.com/paper/2402.05115/) (2024)

## Abstract (arXiv)

Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition. How to effectively use ConvNets for video-based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e., MSRC-12 Kinect gesture dataset (MSRC-12), G3D dataset and UTD multimodal human action dataset (UTD-MHAD) and achieved the state-of-the-art results.