Summary
The applications of computer vision (CV) are continuously increasing along with the enormous demand for real‐time data processing. This visual data processing is done with various compute‐intensive image/video processing algorithms that may belong to traditional approaches or deep learning approaches. This article aims to provide a survey of state‐of‐the‐art hardware platforms and software frameworks for parallel implementation of traditional CV applications. The article discusses various options for hardware platforms for centralized‐computing architecture and edge‐computing architecture, and various software frameworks that can be used to leverage the hardware. This discussion is based on a systematic survey of studies/works that show the use of various hardware platforms and software frameworks in order to achieve real‐time processing for CV algorithms. Based on the survey, some possible future directions are also discussed.
Achieving real-time processing for automated traffic surveillance is a major challenge due to the huge amount of data generated by a large number of surveillance cameras. For this, centralized computing has been a standard architecture for many years.But due to the increasing need for real-time processing and limitations of centralized computing architecture (such as network congestion due to limited bandwidth and the need for costly high-end servers), the paradigm is shifting toward edge processing. This article compares the suitability of two popular but different types of SoCs (System on Chip) (from NVIDIA and Qualcomm) embedded with low-power GPUs as processing units for edge devices. These GPUs can be programmed as general-purpose GPUs (GPGPU) to speed up compute-intensive tasks, so as to achieve real-time processing at the edge of the network. The article also discusses the architectural features and differences between these GPUs and recommends optimization techniques to leverage them. For quantitative comparison, we implement a wrong-way vehicle detection algorithm (using background-subtraction-based moving object detection and Kalman-filter-based trajectory tracking), and optimized it for these SoCs. The experimental results show that real-time processing can be achieved for HD videos with implementations optimized for these SoCs.
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