# Trajectory Prediction in Dynamic Object Tracking: A Critical Study

arXiv: https://arxiv.org/abs/2506.19341 · HTML: https://mlsanity.com/paper/2506.19341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 54 of its 145 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 30 of 54)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (2020)
- [Audio-Visual Scene-Aware Dialog](https://mlsanity.com/paper/1901.09107/) (2019)
- [Learning to Track at 100 FPS with Deep Regression Networks](https://mlsanity.com/paper/1604.01802/) (2016)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018)
- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild](https://mlsanity.com/paper/2104.04029/) (2021)
- [Deep Network Flow for Multi-Object Tracking](https://mlsanity.com/paper/1706.08482/) (2017)
- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021)
- [Graph Neural Networks for Modelling Traffic Participant Interaction](https://mlsanity.com/paper/1903.01254/) (2019)
- [Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters](https://mlsanity.com/paper/1811.10863/) (2018)
- [MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction](https://mlsanity.com/paper/2308.10280/) (2023)
- [Exploit the Connectivity: Multi-Object Tracking with TrackletNet](https://mlsanity.com/paper/1811.07258/) (2018)
- [Map-Adaptive Goal-Based Trajectory Prediction](https://mlsanity.com/paper/2009.04450/) (2020)
- [Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?](https://mlsanity.com/paper/2210.07681/) (2022)
- [Fast Template Matching and Update for Video Object Tracking and Segmentation](https://mlsanity.com/paper/2004.07538/) (2020)

## Abstract (arXiv)

This study provides a detailed analysis of current advancements in dynamic object tracking (DOT) and trajectory prediction (TP) methodologies, including their applications and challenges. It covers various approaches, such as feature-based, segmentation-based, estimation-based, and learning-based methods, evaluating their effectiveness, deployment, and limitations in real-world scenarios. The study highlights the significant impact of these technologies in automotive and autonomous vehicles, surveillance and security, healthcare, and industrial automation, contributing to safety and efficiency. Despite the progress, challenges such as improved generalization, computational efficiency, reduced data dependency, and ethical considerations still exist. The study suggests future research directions to address these challenges, emphasizing the importance of multimodal data integration, semantic information fusion, and developing context-aware systems, along with ethical and privacy-preserving frameworks.