Introduction to Behaviour Detection

What is/are Behaviour Detection?

Behaviour Detection - Monitoring of risk factors is based on incident and event management tools, and misbehaviour detection to address cyber-physical systems’ security gaps. ^[1]

Abnormal Behaviour Detection

In this paper, we propose a multi-sensing modality framework for human fine-grained activity recognition and abnormal behaviour detection by combining skeleton and acceleration data at feature level (so-called feature-level fusion). ^[1] One of these vital services is abnormal behaviour detection based on the data collected from IoT devices within smart homes. ^[2] The capabilities of the UAVAT Framework are demonstrated by presenting the results from two developed test modules targeting endurance testing of a multirotor sUAS and Fault Detection (FD) for abnormal behaviour detection. ^[3] Abnormal behaviour detection algorithm needs to conduct behaviour analysis on the basis of continuous video inclination tracking, and the robustness of the algorithm is reduced for the occlusion of moving targets, the occlusion of the environment, and the movement of targets with the same colour. ^[4] Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. ^[5] Path prediction allows us to estimate the future locations of an object in a given space and is useful in important applications such as surveillance, abnormal behaviour detection, crowd behaviour analysis, traffic control and currently in driver assistance (ADAS) or collision avoidance systems. ^[6] Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection. ^[7] In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. ^[8] In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. ^[9] It has plenty of real-world applications such as health monitoring and abnormal behaviour detection. ^[10] ABSTRACT Spatiotemporal movement pattern discovery has stimulated considerable interest due to its numerous applications, including data analysis, machine learning, data segmentation, data reduction, abnormal behaviour detection, noise filtering, and pattern recognition. ^[11] We describe the platform that has been implemented, and we show that it effectively support abnormal behaviour detection in the real stream of messages within the communication system of the French Railway Company (SNCF). ^[12] Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection. ^[13]

behaviour detection system

After reviewing the state of the art, we show that the physical manifestation of attacks must be considered when evaluating misbehaviour detection systems. ^[1] In this work, we present an operational human cognitive behaviour detection system based on ambulatory EEG signal analysis. ^[2]