An onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter

During the movement or operation of coal mining equipment such as a roadheader, the vibration of the vehicle body can easily cause blurring of the onboard camera video. This leads to a decrease in the precision and reliability of machine vision detection based on the onboard video. In order to solve the above problem, an onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter is proposed. This algorithm consists of three parts: motion estimation, trajectory smoothing, and motion compensation. In the motion estimation stage, the contrast limited adaptive histogram equalization (CLAHE) algorithm is used to enhance the image of the underground roadway. The Shi-Tomasi algorithm is used to obtain the feature points of each image frame. The obtained feature points are tracked and matched by optical flow, and then the motion trajectory of the camera is calculated. In the trajectory smoothing stage, Kalman filtering is used to predict the current time value based on the optimal value of the previous frame of the video. It avoids the problem of pre-storing sampling data of mean filtering and improves the real-time performance of image stabilization. In the motion compensation stage, the jitter video is compensated frame by frame based on the relationship between the original motion path and the smooth path, generating a stable video sequence. The experimental results show the following points: ① After CLAHE enhancement processing, the success rate of feature point matching is increased by 58% compared to the non-enhancement processing and 43% compared to the HE enhancement processing. It indicates that the CLAHE algorithm can effectively improve the matching number of image feature points. ② Through pixel offset analysis, differential image analysis, and peak signal-to-noise ratio (PSNR) analysis, it is verified that the onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter has a good image stabilization effect. ③ Compared with the traditional HE+mean filtering algorithm, the algorithm based on CLAHE and Kalman filter reduces the overall time consumption of processing 100 frames of video images by 0.379 seconds, effectively improving the real-time performance of the video stabilization while removing jitter.