Fully pipelined VLSI architecture of a real-time block-based object detector for intelligent video surveillance systems

Tuan, Min-Chun; Chen, Shih-Lun

doi:10.1109/icis.2015.7166585

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…The simplest approach to ROI-based coding is to drop non-ROI blocks and encode/transmit only the ROI blocks [12]. This approach reduces encoder energy and overall data volume as non-ROI blocks are not encoded or transmitted.…”

Section: Prior Work On Roi-based Codingmentioning

confidence: 99%

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

Mukhopadhyay

2016

2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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This paper presents a lightweight video sensor node for moving object surveillance using region-of-interest (ROI) based coding and an on-line multi-parameter rate controller. The proposed ROI-based coding scheme determines ROI blocks, pre-processes non-ROI blocks using bit-truncation, and encodes all blocks using Motion JPEG. The on-line rate controller modulates the parameters of the ROI-based coding scheme to match the encoded data rate and transmission data rate under the variations in channel bandwidth and input video content. The low-complexity hardware of the ROI-based coding scheme reduces computation energy, and the on-line rate controller minimizes buffer requirement. The sensor node is designed in 130nm CMOS and prototyped in a Virtex-V FPGA. Simulations show that, under the same ROI quality, the proposed approach reduces system energy by 61% compared to H.264/AVC. Figure 1: ROI-based rate controller design (a) tied to H.264/AVC and (b) based on a simple encoder (MJPEG).

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Section: Prior Work On Roi-based Codingmentioning

confidence: 99%

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

Mukhopadhyay

2016

2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…Computer vision is the ability of computers to interpret and understand visual data from the environment. This technology has diverse applications, ranging from facial recognition [1] and object detection [2] to self-driving vehicles. The widespread use of cameras has generated an immense number of photos and videos.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

Chi,

Chen,

Lin

et al. 2024

Sensors

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The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

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A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Siddiqui,

Siddiqi

2023

Communications in Computer and Information Science

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Fully pipelined VLSI architecture of a real-time block-based object detector for intelligent video surveillance systems

Cited by 9 publications

References 13 publications

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

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