Pipeline-Oriented Visual Reconstruction Frameworks for High-Volume Recognition Workloads

Abstract

The large-scale use of visual recognition systems such as high-volume systems applied to large scale surveillance, intelligent transportation, and industrial automation is increasingly subject to severe limitation issues in the availability of bandwidth, processing latency, and computational scalability. Traditional end-to-end deep learning designs, albeit good at just a few controlled conditions, tend to be less adaptable and not at all scalable to a distributed and high-throughput environment because of their monolithic structure. To overcome these shortcomings, in this paper, a pipeline-based visual reconstruction system adapted to scalable and automated recognition tasks will be proposed. The proposed methodology breaks down the visual processing pipeline into stages of modular pipelines, such as visual processing phases: compression-understanding reconstruction, enhancement, feature extraction and recognition, leading to the possibility of separately optimizing pipeline stages and distributing their execution. An elaborate methodology has been described that reflects the policy of pipeline design, flexible construction policies and coordination processes of deployment on heterogeneous computing systems. Experimental assessments on both high-volume and compressed visual processes have shown that the proposed pipeline-based system achieves recognition performance on the level of a classical end-to-end systems, and it offers greatly enhanced system throughput rates, end-to-end latency rates, and scalability. Additionally, the modular pipeline design provides the ability to utilize the resources efficiently and withstand the variances in workload. The findings prove pipeline-based visual reconstruction is an effective and viable solution to large scale recognition systems and it offers a compromise in terms of performance on a large scale with scalability. The article emphasizes the opportunities of pipeline automation as one of the main architectural principles of the next generation distributed visual recognition applications.