# CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis

arXiv: https://arxiv.org/abs/1809.05782 · HTML: https://mlsanity.com/paper/1809.05782/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 30 of 39)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography](https://mlsanity.com/paper/2103.15293/) (2021)
- [TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance](https://mlsanity.com/paper/2209.12386/) (2022)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [A Study of Situational Reasoning for Traffic Understanding](https://mlsanity.com/paper/2306.02520/) (2023)
- [AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond](https://mlsanity.com/paper/2509.26636/) (2025)
- [Toward Planet-Wide Traffic Camera Calibration](https://mlsanity.com/paper/2311.04243/) (2023)
- [C-Drag: Chain-of-Thought Driven Motion Controller for Video Generation](https://mlsanity.com/paper/2502.19868/) (2025)
- [ADNet: Temporal Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2104.06653/) (2021)
- [A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture](https://mlsanity.com/paper/2303.06202/) (2023)
- [TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios](https://mlsanity.com/paper/2311.18839/) (2023)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Accident Anticipation via Temporal Occurrence Prediction](https://mlsanity.com/paper/2510.22260/) (2025)
- [CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis](https://mlsanity.com/paper/2409.19942/) (2024)
- [Learning collision risk proactively from naturalistic driving data at scale](https://mlsanity.com/paper/2505.13556/) (2025)
- [Utilizing Background Knowledge for Robust Reasoning over Traffic Situations](https://mlsanity.com/paper/2212.07798/) (2022)
- [Towards Vision Zero: The TUM Traffic Accid3nD Dataset](https://mlsanity.com/paper/2503.12095/) (2025)
- [AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports](https://mlsanity.com/paper/2503.20654/) (2025)
- [Detection of road traffic crashes based on collision estimation](https://mlsanity.com/paper/2207.12886/) (2022)
- [Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba](https://mlsanity.com/paper/2504.03235/) (2025)
- [Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods, Datasets, and Future Directions](https://mlsanity.com/paper/2505.07611/) (2025)
- [Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation](https://mlsanity.com/paper/2506.05399/) (2025)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance](https://mlsanity.com/paper/2509.09730/) (2025)
- [DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System](https://mlsanity.com/paper/2510.26004/) (2025)

## Abstract (arXiv)

This paper presents a novel dataset for traffic accidents analysis. Our goal is to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads. Through the analysis of the proposed dataset, we observed a significant degradation of object detection in pedestrian category in our dataset, due to the object sizes and complexity of the scenes. To this end, we propose to integrate contextual information into conventional Faster R-CNN using Context Mining (CM) and Augmented Context Mining (ACM) to complement the accuracy for small pedestrian detection. Our experiments indicate a considerable improvement in object detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage for the paper is https://goo.gl/cqK2wE