{"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/cadp-a-novel-dataset-for-cctv-traffic-camera","title":"CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis","arxiv_id":"1809.05782","date":"2018-09-16","proceeding":null,"authors":["Ankit Shah","Jean Baptiste Lamare","Tuan Nguyen Anh","Alexander Hauptmann"],"abstract":"This paper presents a novel dataset for traffic accidents analysis. Our goal\nis to resolve the lack of public data for research about automatic\nspatio-temporal annotations for traffic safety in the roads. Through the\nanalysis of the proposed dataset, we observed a significant degradation of\nobject detection in pedestrian category in our dataset, due to the object sizes\nand complexity of the scenes. To this end, we propose to integrate contextual\ninformation into conventional Faster R-CNN using Context Mining (CM) and\nAugmented Context Mining (ACM) to complement the accuracy for small pedestrian\ndetection. Our experiments indicate a considerable improvement in object\ndetection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate\nthe performance of accident forecasting in our dataset using Faster R-CNN and\nan Accident LSTM architecture. We achieved an average of 1.684 seconds in terms\nof Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage\nfor the paper is https://goo.gl/cqK2wE","url_abs":"http://arxiv.org/abs/1809.05782v2","url_pdf":"http://arxiv.org/pdf/1809.05782v2.pdf","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":"cadp-a-novel-dataset-for-cctv-traffic-camera","repo_url":"https://github.com/ankitshah009/CarCrash_forecasting_and_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[{"slug":"cadp","name":"CADP","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.05782","atlas_url":"https://app.syntology.ai/?focus=1809.05782","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}