{"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/multi-timescale-trajectory-prediction-for","title":"Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection","arxiv_id":"1908.04321","date":"2019-08-12","proceeding":null,"authors":["Royston Rodrigues","Neha Bhargava","Rajbabu Velmurugan","Subhasis Chaudhuri"],"abstract":"A classical approach to abnormal activity detection is to learn a representation for normal activities from the training data and then use this learned representation to detect abnormal activities while testing. Typically, the methods based on this approach operate at a fixed timescale - either a single time-instant (eg. frame-based) or a constant time duration (eg. video-clip based). But human abnormal activities can take place at different timescales. For example, jumping is a short term anomaly and loitering is a long term anomaly in a surveillance scenario. A single and pre-defined timescale is not enough to capture the wide range of anomalies occurring with different time duration. In this paper, we propose a multi-timescale model to capture the temporal dynamics at different timescales. In particular, the proposed model makes future and past predictions at different timescales for a given input pose trajectory. The model is multi-layered where intermediate layers are responsible to generate predictions corresponding to different timescales. These predictions are combined to detect abnormal activities. In addition, we also introduce an abnormal activity data-set for research use that contains 4,83,566 annotated frames. Data-set will be made available at https://rodrigues-royston.github.io/Multi-timescale_Trajectory_Prediction/ Our experiments show that the proposed model can capture the anomalies of different time duration and outperforms existing methods.","url_abs":"https://arxiv.org/abs/1908.04321v1","url_pdf":"https://arxiv.org/pdf/1908.04321v1.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":[],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"activity-detection","task_name":"Activity Detection"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[],"datasets_introduced":[{"slug":"iitb-corridor","name":"IITB Corridor","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-corridor","task":"Anomaly Detection","dataset":"Corridor","model":"Multi-timescale Prediction","rank_in_archive_order":3,"of":3,"metrics":{"AUC":"67.12%"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-shanghaitech","task":"Anomaly Detection","dataset":"ShanghaiTech","model":"Multi-timescale Prediction","rank_in_archive_order":25,"of":31,"metrics":{"AUC":"76.03%"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-avenue","task":"Video Anomaly Detection","dataset":"HR-Avenue","model":"Multi-timescale Prediction","rank_in_archive_order":3,"of":11,"metrics":{"AUC":"88.33"},"uses_additional_data":false},{"leaderboard":"/sota/video-anomaly-detection-on-hr-shanghaitech","task":"Video Anomaly Detection","dataset":"HR-ShanghaiTech","model":"Multi-timescale Prediction","rank_in_archive_order":7,"of":14,"metrics":{"AUC":"77.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.04321","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}