To improve the production/distribution efficiency of perishable products with short lives under Make-To-Order (MTO) mode, considering the operational costs of business and customer demand for freshness degree of delivered products, a bi-objective model was established to coordinate the production scheduling and vehicle routing with minimum freshness limitations, which aims to minimize the total distribution cost and maximize the total freshness degree of delivered products. And an elitist nodominated sorting genetic algorithm with chromosomes encoded by two substrings was devised to optimize the proposed model. Firstly, the customers' time windows were described and freshness degrees of delivered products were defined with average degree level for multiple kinds of products. The bi-objective model was constructed to schedule production and delivery simultaneously. Then, the hard constraints and two objective functions were transformed. Chromosomes were encoded by two substrings and the computation framework of elitist nodominated sorting genetic algorithm with some key operators was adopted to solve the proposed model. Finally, the proposed algorithm was tested with the comparison of Pareto based simulated annealing on a numerical example. The simulation results show that the two objectives have a trade-off conflict and the proposed algorithm can provide Pareto optimal solutions. The sensitivity analysis of minimum limitation of freshness degree demonstrates that the two objectives are affected significantly when fewer vehicles are put into use.

Image visual saliency analysis algorithms have achieved satisfactory performance on existing datasets, but these datasets have two major problems. Firstly, most of the images contain only one salient object. Secondly, users' cognition of multiple salient objects in the same image was ignored during building salient objects' ground truth. The above problems result in that the performance of saliency analysis algorithms used in the real applications cannot be reflected by the evaluation on the existing datasets. So in this paper, a novel method of labeling the ground truth of salient objects was proposed. Firstly, a software to collect users' cognition of the important values of multiple salient objects in each image was designed and implemented. Then, according to the collected data from each user, the ground truth map represented as a gray scale image was created by manually labeling the regions covered by the salient objects. The pixel value of each region equals to the collected saliency in the first step. Based on the improved ground truth labeling method, a salient object dataset contains 1000 multi-object images was built. A ground truth map for each image was created to record users' cognition of the objects' saliencies. Then 10 state-of-the-art saliency analysis algorithms on existing datasets and the established dataset were compared. The experimental results show that these algorithms' performances are greatly reduced on the established dataset, such as the Area Under Curve of Receiver-Operating Characteristic (ROC-AUC) has a greatest decline of more than 0.5. The results prove the problems of existing datasets and the demand of building a new dataset, and point out the insufficiency of saliency analysis algorithms on complex images with multiple salient objects.

Most of the current trajectory-based abnormal behavior detection algorithms do not consider the internal information of the trajectory, which might lead to a high false alarm rate. An abnormal behavior detection method based on trajectory segment using the topic model was presented. Firstly, the original trajectories were partitioned into trajectory segments according to turning angles. Secondly, the behavior characteristic information was extracted by quantifying the observations from these segments into different visual words. Then the time-space relationship among the trajectories was explored by Latent Dirichlet Allocation (LDA) model. Finally, the behavior pattern analysis and the abnormal behavior detection could be implemented by learning the corresponding generative topic model combined with the Bayesian theory. Simulation experiments of behavior pattern analysis and abnormal behavior detection were conducted on two video scenes, and different kinds of abnormal behavior patterns were detected. The experimental results show that, combining with trajectory segmentation, the proposed method can dig the internal behavior characteristic information to identify a variety of abnormal behavior patterns and improve the accuracy of abnormal behavior detection.