{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prompttad-object-prompt-enhanced-traffic","title":"PromptTAD: Object-Prompt Enhanced Traffic Anomaly Detection","arxiv_id":null,"date":"2025-05-22","proceeding":"IEEE Robotics and Automation Letters (RA-L) 2025 5","authors":["Hao Qiu","Xiaobo Yang","and Xiaojin Gong"],"abstract":"Ego-centric Traffic Anomaly Detection (TAD) aims to identify abnormal events in videos captured by dashboard-mounted cameras in vehicles. Compared to anomaly detection in roadside surveillance videos, ego-centric TAD poses greater challenges due to the dynamic backgrounds caused by vehicle motion. Previous frame-level methods are often vulnerable to interference from these dynamic backgrounds and struggle to detect small objects located at a distance or off-center. To address these challenges, we propose an object-prompt enhanced method that integrates detected traffic objects into a frame-level TAD framework. Our approach introduces an object-prompt scheme comprising an object prompt encoder, along with two cross-attention-based aggregation modules: an instance-wise aggregation module for fusing information between object instances and the scene, and a relation-wise aggregation module for capturing relationships inter-objects. Additionally, we design an instance-level loss to supervise anomaly detection at the object level. Our method effectively mitigates interference from dynamic backgrounds, improves the detection of distant or off-center anomalies, and enables precise spatial localization of anomalies. Experimental results on the DoTA and DADA-2000 datasets demonstrate that our method achieves state-of-the-art performance.","url_abs":"https://ieeexplore.ieee.org/document/11008816/authors#authors","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008816","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"prompttad-object-prompt-enhanced-traffic","repo_url":"https://github.com/Smartpearkorl/PromptTAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"online-video-anomaly-detection","task_name":"Online Video Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}