Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers’ hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.