Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information (CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O( mN log N) to O( N) ( N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.

In order to solve the image degradation in the non-uniform dust environment with multiple scattering lights, a slices reconstruction method for single image dedusting was proposed. Firstly, the slices along the depth orientation were produced based on McCartney model in dust environment. Secondly, the joint dust detection method was used to detect dust patches in the slices where non-dust areas were reserved but the dust zones were marked as the candidate detected areas of the next slice image. Then, an image was reconstructed by combining these non-dust areas of each slice and the dust zone of the last slice. Finally, a restored image was obtained by a fast guided filter which was applied to the reconstructed area. The experimental results prove that the proposed restoration method can effectively and quickly get rid of dust in the image, and lay the foundation of object detection and recognition work based on computer vision in dust environment.