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Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper proposes a student behavior recognition system based on skeleton pose estimation and person detection. First, consecutive frames captured with a classroom camera were used as the input images of the proposed system. Then, skeleton data were collected using the OpenPose framework. An error correction scheme was proposed based on the pose estimation and person detection techniques to decrease incorrect connections in the skeleton data. The preprocessed skeleton data were subsequently used to eliminate several joints that had a weak effect on behavior classification. Second, feature extraction was performed to generate feature vectors that represent human postures. The adopted features included normalized joint locations, joint distances, and bone angles. Finally, behavior classification was conducted to recognize student behaviors. A deep neural network was constructed to classify actions, and the proposed system was able to identify the number of students in a classroom. Moreover, a system prototype was implemented to verify the feasibility of the proposed system. The experimental results indicated that the proposed scheme outperformed the skeleton-based scheme in complex situations. The proposed system had a 15.15% higher average precision and 12.15% higher average recall than the skeleton-based scheme did.

This article considers a two-machine flow-shop scheduling problem of minimizing total late work. Unlike tardiness, which is based upon the difference between the job completion time and the due date, the late work of a job is defined as the amount of work not completed by its due date. This article first shows that the problem remains non-deterministic polynomial time (NP) hard even if all jobs share a common due date. A lower bound and a dominance property are developed to design branch-and-bound algorithms. Computational experiments are conducted to assess the performance of the proposed algorithms. Numerical results demonstrate that the lower bound and dominance rule can help to reduce the computational efforts required by exploring the enumeration tree. The average deviation between the solution found by tabu search and the proposed lower bound is less than 3%, suggesting that the proposed lower bound is close to the optimal solution.

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