# Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking

arXiv: https://arxiv.org/abs/2411.17251 · HTML: https://mlsanity.com/paper/2411.17251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [SwinTrack: A Simple and Strong Baseline for Transformer Tracking](https://mlsanity.com/paper/2112.00995/) (2021)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)
- [Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism](https://mlsanity.com/paper/1708.02843/) (2017)
- [Efficient Visual Tracking with Exemplar Transformers](https://mlsanity.com/paper/2112.09686/) (2021)
- [3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention](https://mlsanity.com/paper/2203.10926/) (2022)
- [Temporal Dynamic Graph LSTM for Action-driven Video Object Detection](https://mlsanity.com/paper/1708.00666/) (2017)
- [Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking](https://mlsanity.com/paper/1907.05315/) (2019)
- [YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems](https://mlsanity.com/paper/2410.22898/) (2024)

## Abstract (arXiv)

The detection and tracking of small, occluded objects such as pedestrians, cyclists, and motorbikes pose significant challenges for traffic surveillance systems because of their erratic movement, frequent occlusion, and poor visibility in dynamic urban environments. Traditional methods like YOLO11, while proficient in spatial feature extraction for precise detection, often struggle with these small and dynamically moving objects, particularly in handling real-time data updates and resource efficiency. This paper introduces DGNN-YOLO, a novel framework that integrates dynamic graph neural networks (DGNNs) with YOLO11 to address these limitations. Unlike standard GNNs, DGNNs are chosen for their superior ability to dynamically update graph structures in real-time, which enables adaptive and robust tracking of objects in highly variable urban traffic scenarios. This framework constructs and regularly updates its graph representations, capturing objects as nodes and their interactions as edges, thus effectively responding to rapidly changing conditions. Additionally, DGNN-YOLO incorporates Grad-CAM, Grad-CAM++, and Eigen-CAM visualization techniques to enhance interpretability and foster trust, offering insights into the model's decision-making process. Extensive experiments validate the framework's performance, achieving a precision of 0.8382, recall of 0.6875, and mAP@0.5:0.95 of 0.6476, significantly outperforming existing methods. This study offers a scalable and interpretable solution for real-time traffic surveillance and significantly advances intelligent transportation systems' capabilities by addressing the critical challenge of detecting and tracking small, occluded objects.