# Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction

arXiv: https://arxiv.org/abs/2007.00862 · HTML: https://mlsanity.com/paper/2007.00862/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs](https://mlsanity.com/paper/2109.10257/) (2021)
- [SVG-Net: An SVG-based Trajectory Prediction Model](https://mlsanity.com/paper/2110.03706/) (2021)
- [Learning Social Cost Functions for Human-Aware Path Planning](https://mlsanity.com/paper/2407.10547/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Predicting Human Trajectories by Learning and Matching Patterns](https://mlsanity.com/paper/2104.10241/) (2021)
- [Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting](https://mlsanity.com/paper/2407.21185/) (2024)
- [Learning Social Heuristics for Human-Aware Path Planning](https://mlsanity.com/paper/2509.02134/) (2025)

## Abstract (arXiv)

We propose a Convolutional Neural Network-based approach to learn, detect,and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly,the proposed approach unveils the obscurity in the previous use of pooling layer, presenting a way to intuitively explain the decision-making process.