## 6 Ongoing Experimental Applications: Computer Vision-Based Monitoring  
There are also cases where the use of accelerometric sensors is not always optimal for structural monitoring. In particular, when the monitoring of displacement (or velocity) is needed, the process of double integration of the signal from the accelerometric sensors is biased by the baseline offset of the digital recording and often produces unrealistic results. The origin of such offsets is diverse and can  
Figure 7: Output from the MPwise processing of seismic array data for site assessment (note, this is the Graphical User Interface (GUI) developed for the SIBYL project). Top left) The data source and parameter menu allows the user to configure different parameters, such as the number of stations, network code, sampling rate, channel identifier, digitizer and sensor specific constants, etc. Top right) Overview of the array geometry. Bottom left) Real-time processing of the array measurements using the extended spatial autocorrelation (ESAC) method [29] to obtain a Rayleigh wave dispersion curve. Bottom right) SVD (Single Value Decomposition) inversion to obtain a 1D shear wave velocity model, as well as a quality control provided by [PERSON], model and data resolution matrices. be classified in terms of errors due to instrumental instability (non-linear instrumental response, limited resolution of the measuring system, insufficient sampling rate, level of electronic noise, etc. ), background noise (depending on the target site), the estimation of the real initial acceleration, velocity and displacement values, and data manipulation. Alternative measurement techniques for displacement include, for instance, analog or digital strain sensors, which offer good performance, but over a reduced working range. Several laboratory-based experimental set-ups (e.g., [30; 31]) have demonstrated the potential of image-based monitoring. Displacement measurements of high-rise buildings based on conventional imaging have been proposed by [32; 33], the latter using high-speed linear cameras, while [34; 35] proposed the dynamic monitoring of slender structures using various computer-vision techniques. Most of these approaches involve 1-D or 2-D measurements, but with more complex arrangements, 3-D structural monitoring is also possible, as showed by [36; 37; 38]. The mentioned techniques and approaches can be used to integrate the pre-event assessment of fragility for residential structures, and to collect in real-time information about the health state of the structure during and after an earthquake (see also [32] for a specific application to damage detection). Furthermore, the prompt observation of, for example, overthrown furniture in the building or significant internal structural and non-structural damage would, in many cases, assist search and rescue operations in the aftermath of a damaging event. Several laboratory tests of a prototype displacement measurement set-up using MPwise units connected to cameras have been carried out (see Figure 8). The experiment entails a visual target being fixed to a 1-D shaking table simulating realistic ground shaking. A low-cost USB camera, connected to the MPwise device, tracks in real time the position of the visual target and provides a repeated (at 5 frames per second) estimate of the observed target's displacement. The software work-flow for this application is depicted in Figure 3 (right). While the scale of the experiment is not representative of actual applications, the designed laboratory test allowed us to evaluate the feasibility of the ongoing efforts. The next step will include repeating this test with a much higher frame rate of about 90 frames/s, and extending it to real cases, such as its installation in buildings and other structures. Two approaches will be tested; for the first approach, the camera and target will be inside the building, the camera mounted on the floor and the target on the ceiling to provide information about the inter-storey drift.