With image analysis as the core for multitarget detection and intelligent tracking, mostly applying the Faster R-CNN or YOLO framework, the MOTA score for multitarget tracking is low in the face of complex working environments. Therefore, further research into computer vision techniques is carried out to design new multitarget detection and intelligent tracking methods. Based on the small-aperture imaging model, the principle of lens distortion was analyzed, and a camera calibration and image calibration scheme was designed to obtain effective environmental images. The attention mechanism is introduced to optimise the structure of deep learning networks, and a computer vision detection algorithm based on this is applied to complete regional multitarget detection. The distance between each target and the body is then measured in combination with binocular vision principles. Finally, the spatiotemporal context algorithm is applied to perform simulation calculations to obtain the multitarget intelligent tracking results. The experimental results show that the mean MOTA score of the proposed technique is 0.87 in the night environment, which is 24.14% and 28.374% better than the neural network-based and machine vision-based tracking methods, respectively; in the daytime environment, the mean MOTA score of the multitarget tracking results of the technique is 0.94, which is 28.72%, and the mean MOTA score of 0.94 for the multitarget tracking results in the daytime environment was 28.72% and 22.34% higher than the other two methods.

With the rapid development of software-defined network and network function virtualization technology, the scale of infrastructure and the number of available resources in cloud platforms continue to grow. It is also be used in AD Visualiser. AD is a visualisation tool for displaying the atomic decomposition (AD) of OWL ontologies. As the size of ontologies increases, ontology engineers become more difficult to understand and reuse ontologies. Atomic decomposition (AD) is a modular structure to help ontology engineers modularly manage ontologies. It decomposes ontologies into sets of atoms, with dependency, based on modules that provide strong logical guarantees (such as locality-based modules). This paper describes the design and implementation process of AD Visualiser and discusses its usability for ontology engineers in their daily work. For example, using AD Visualiser, ontology engineers avoid choosing signatures and determining the extraction results. They can extract modules very simply and faster. Besides, the graph of AD’s modular structure should be helpful for engineers to intuitively explore and comprehend ontologies.