Research And Application Of Video Tracking In Mine Base On TLD

Computer vision technology is the technology to detect, track and measure the target in video through combining the capture function of the camera and processing function of the computer. The video surveillance can improve the safety and management level of the underground work. Among all the work in mine, we must strengthen the rock after mining the tunnel. Bolt supporting is the common way to strengthen the rock, and the quality of bolt supporting is related to the safety of the work underground. The evaluation of the supporting quality includes the bolt material, the depth of the bolting hole and the distance between the bolts. The depth of the bolting hole is an important factor affecting the quality of the supporting. In order to ensure the depth of the bolting hole, we must ensure the count of the bolts.Counting the bolts through the computer vision technology is the subject of the paper.We must track the roof bolter in order to count the bolts.The video in mine is lacking of color information and the illumination is complex.Having researched several tracking algorithms, we adopt the TLD algorithm to track the roof bolter in mine based on the special issue. The main contents in our paper as follows.(1) Adopting the Lucas-Kanade algorithm based on the sparse optical flow to track the roof bolter. Selecting the corners and uniform points as the tracking points. To filter the result points, we calculate the forward errors between two adjacent image frames and the matching similarity of the area around the result points, and compare them with the threshold.(2) Adopting the classification method to detect the target. The combination of the global search and local search is adopted to detect the target, if the tracking is successful in last frame, we use the local search, otherwise, we use the global search. The variance that contains global information, the random forest based on image features and the template matching method which has high reliability are used to detect the target through classing.(3) The PN learning method is adopted to update the classifier. Adopt the linear predicting method to predict the location of the target when the algorithm fails. We draws thetrajectory of the roof bolter according to the historical information, and complete the task of the counting. To increase the speed of the algorithm, we use the Openmp library to realize the parallel processing of tracking and detection.The results shows that the improved TLD algorithm in our paper can track the roof bolter,count the bolts, and we can get better performance using the improved algorithm.