Student Detection and People Counting System Based on Apriori Algorithm

Due to the continuous progress of machine vision and image processing science and technology and the rise of deep learning and artificial intelligence technology in recent years, the development of digital computing science and technology methods has been greatly promoted. The purpose of this paper is to design a student detection and people counting system based on the Apriori algorithm. Four algorithms are proposed to optimize and improve Apriori algorithm, namely sampling-based method, partition-based method and hashing method, and ways to reduce the number of transactions. In order to improve the training speed and test results of the algorithm, the loss function of the Apriori algorithm is improved, and the classification loss related to the detection task, the return loss of the detection frame, the return loss of the face landmark, and the return loss of the useless point are retained to eliminate the face density in the work. etc., optimize the multi-task loss function. Finally, three recorded videos are tested using four improved algorithms. The test results show that the hash-based method has a correct rate of 100% and a false positive rate of 0. It is the most optimized and improved algorithm among the four Apriori Efficient methods. The partition-based method has a correct rate of 85.71% and a false positive rate of 14.29%. The improvement results are minimal.