Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation

Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

A Matching Method of Heterogeneous Database based on SOM and BP Neural Network

In the process of data integration among heterogeneous databases, it is significantly important to analyze the identical attributes and characteristics of the databases. However, the existing main data attribute matching model has the defects of oversize matching space and low matching precision. Therefore, this paper puts forward a heterogeneous data attribute matching model on the basis of fusion of SOM and BP network through analyzing the attribute matching process of heterogeneous databases. This model firstly matches the heterogeneous data attributes in advance by SOM network to determine the centre scope of attribute data to be matched. Secondly, the accurate match will be carried out through BP network of the standard heterogeneous data various attribute center. Finally, the matching result of the relevant actual database shows that this model can effectively reduce the matching space in the case of complex pattern. As for the large-scale data matching, the matching accuracy is relatively high. The average precision is 89.52%, and the average recall rate is 100%.

Gated Object-Attribute Matching Network for Detailed Image Caption

Image caption enables computers to generate a text description of images automatically. However, the generated description is not good enough recently. Computers can describe what objects are in the image but cannot give more details about these objects. In this study, we present a novel image caption approach to give more details when describing objects. In detail, a visual attention-based LSTM is used to find the objects, as well as a semantic attention-based LSTM is used for giving semantic attributes. At last, a gated object-attribute matching network is used to match the objects to their semantic attributes. The experiments on the public datasets of Flickr30k and MSCOCO demonstrate that the proposed approach improved the quality of the image caption, compared with the most advanced methods at present.