Visible to the public Incremental knowledge acquisition and self-learning for autonomous video surveillance

TitleIncremental knowledge acquisition and self-learning for autonomous video surveillance
Publication TypeConference Paper
Year of Publication2017
AuthorsNawaratne, R., Bandaragoda, T., Adikari, A., Alahakoon, D., Silva, D. De, Yu, X.
Conference NameIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Date Publishedoct
Keywordsaction recognition, anomaly detection, autonomous video surveillance, benchmark video dataset, Change detection, Clustering algorithms, Cognition, event detection, feature extraction, hazard detection, Hazards, Heuristic algorithms, Human Behavior, imperative security, incremental knowledge acquisition, incremental learning, incremental machine learning, Industry 4.0, industry/smart factory settings, intelligent high-level video analysis, Intelligent Video Analysis, knowledge acquisition, object detection, pubcrawl, Resiliency, safety purpose, Scalability, self-learning, Self-Organizing Maps, standard machine learning pipelines, unlabeled video streams, unsupervised learning, unsupervised machine learning, video feed, video frame, video signal processing, video stream, video streaming, video surveillance, video surveillance systems

The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.

Citation Keynawaratne_incremental_2017