A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Opinion <s> Abstract Our research works focuses on the design and implementation of a novel ubiquitous multi context-aware mobile phone sensing network for mitigation of crowd disasters using machine-to-machine (M2M) communications. A mobile sensor network system integrated with wireless multimedia sensor networks (WMSNs) was designed for effective prediction of a stampede during crowd disasters. This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data. Real-time crowd dynamics modeling and real-time activity modeling have been achieved by implementing the algorithms developed for Context Acquisition and multi-context fusion. Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus. The implementation of the proposed architecture in Android smartphone provides light-weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation and generation of an in time alert to take measures to avoid the occurrence of a stampede. The system has been tested and illustrated within a group of people for stampede prediction by using empirically collected data. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Opinion <s> We propose a novel context aware anomaly detection process applied to human behaviour.We evaluate our method upon 2 different datasets.We utilise a novel social connections metric and a scene model as contextual information.The use of scene contextual information improves the detection of subtle anomalies.The use of social contextual information improves the detection of abnormal behaviour. This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour. <s> BIB002
This type of paper reports on a personal view of the author on true satisfaction or dissatisfaction on a specific method, or technique. Such papers present their view or feelings on how certain technique(s) should be realised. For the purpose of RQ Q4. Figure 6 (a) provides answers to the Q4, four (4%) addressed issues related to DM in a crowd out of all the 75 selected primary studies, BIB001 ; automatic video analysis of crowd disaster ; and crowded surveillance scenes BIB002 , though only BIB001 proposed CA framework based on crowd abnormality monitor (CAM) and employed AR with machine learning approach to determine stampede occurrence. BIB001 further remarked that higher ARAC is a necessity for effective and reliable stampede prediction in a crowded area. However, recently further remarked that HAR schemes classification accuracy is relatively low as well. Therefore, it is imperative to identify causes for the low accuracy and profer solution to improve the accuracy to prevent negative consequence the low accuracy can cause in a real life. Research types is summarised in Figure 6 (c).