Internet of Things: Analysis of suspicious behaviour in a surveillance camera network

Abstract

The work presented in this Ph.D. thesis focuses on developing a large-scale distributed video surveillance system for tracking suspicious moving objects and analysing their behaviour in an IoT environment. To date, video surveillance systems still face the problem of unnecessary and redundant data caused by multiple detections of the same events by several cameras due to overlapping fields of view. These problems affect not only the increase in processing, storage, and communication resources consumed but also the quality of the tracking, the quality of the behavioural analysis of the tracked objects, and the real-time operation of the system. These effects are particularly exacerbated by dense deployments of large-scale camera networks. To address these issues, we proposed a new distributed and collaborative tracking system. This system aims to improve the quality of monitoring and reduce the cost of data processing by reducing the number of active cameras and reducing communicating data. The proposed system operates in two steps: (i) Electing a leader who has the best view of the detected object among the neighbouring cameras. (ii) Choosing the best assistants from neighbouring cameras to maximise detection when the leader's vision is insufficient to track the object. Only the leader and their assistants are active. The other neighbouring cameras remain in an inactive state. To improve real-time data processing, the system's load is distributed throughout the IoVT computing architecture. We proposed two methods of grouping cameras based on the FoV overlap area criterion instead of the radio and distance criterion to reduce the coordination mechanism's complexity, ensure the feasibility of their operation in large-scale networks, and restrict the communication range between cameras. The first proposed technique is based on the ascending hierarchical classification algorithm. This method mainly focuses on grouping cameras that have maximum overlap. Unfortunately, the method has only partial knowledge about the network and its state, i.e., it only knows the maximum overlap. The other overlaps are not taken into account and are completely neglected. To exceed this limit, we proposed a second grouping technique that groups not only the two most overlapping cameras but also all overlapping cameras with as many cameras as possible. To find this group, we used the Bron-karboch Clique-based search algorithm. We also proposed a new and efficient indexing mechanism based on the tree structure. The proposed mechanism aims to index the massive data generated by large-scale cameras network to organise it appropriately to reduce the search time as much as possible to ensure real-time system operation. This structure is based on recursive partitioning of space using the k-means clustering algorithm to effectively separate space into non-overlapping subspace to improve search and discovery algorithm results. The results obtained demonstrate the effectiveness of the proposed methods in terms of tracking quality, amount of network data, energy consumption and real-time operation compared to the conventional system.