Real-time implementation of panoramic mosaic camera based on FPGA

Zhou, Wensong; Liu, Yunhui; Lyu, Congyi; Zhou, Weihua; Peng, Jianqing; Yang, Ruijia; Shang, Haiyang

doi:10.1109/rcar.2016.7784026

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Panoramic mosaic algorithm [36] is used in conjunction with six low-light cameras. The original distorted images from four directions [37] around the vehicle are first dedistorted, and then, the perspective transformation is carried out. After that, the image mosaic [38] is realized.…”

Section: Panoramic Mosaic Algorithmmentioning

confidence: 99%

Visual driving assistance system based on few-shot learning

et al. 2021

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With the increase of vehicles and the diversification of road conditions, people pay more attention to the safety of driving. In recent years, autonomous driving technology by Franke et al. (IEEE Intell Syst Their Appl 13(6):40-48, 1998) and unmanned driving technology by Zhang et al. (CAAI Trans Intell Technol 1(1):4-13, 2016) have entered our field of vision. Both automatic driving by Levinson et al. (Towards fully autonomous driving: Systems and algorithms, 2011) and unmanned driving by Im et al. (Unmanned driving of intelligent robotic vehicle, 2009) use a variety of sensors to collect the environment around the vehicle, and use a variety of decision control algorithms to control the vehicle in motion. The visual driving assistance system by Watanabe, et al. (Driving assistancesystem for appropriately making the driver recognize another vehicle behind or next to present vehicle, 2010), used in conjunction with the target recognition algorithm by Pantofaru et al. (Object recognition by integrating multiple image segmentations, 2008)), will provide drivers with real-time environment around the vehicle. In recent years, few-shot learning by Li et al. (Comput Electron Agric 2:2, 2020) has become a new direction of target recognition algorithm, which reduces the difficulty of collecting training samples.In this paper, on one hand, several low-light cameras with fish-eye lenses are used to collect and reconstruct the environment around the vehicle. On the other hand, we use infrared camera and lidar to collect the environment in front of the vehicle. Then, we use the method of few-shot learning to identify vehicles and pedestrians in the forward-view image. In addition, we develop the system on embedded devices according to miniaturization requirements. In conclusion, the system will adapt to the needs of most drivers at this stage, and will effectively cooperate with the development of automatic driving and unmanned driving.

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Section: Panoramic Mosaic Algorithmmentioning

confidence: 99%

Visual driving assistance system based on few-shot learning

et al. 2021

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“…A smart camera architecture is presented by Zhou et al. [50] which is implemented on the Zynq 7020 FPGA board using the Sum of Absolute Differences (SAD) based Mosaic Algorithm. The use of SAD based Mosaic Algorithm in an unoptimised way increases the hardware requirements.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The main reason for large hardware requirements by this architecture than existing those of the proposed is the use of generalized IP Cores to implement total architecture without proper optimizations. A smart camera architecture is presented by Zhou et al[50] which is implemented on the Zynq 7020 FPGA board using the Sum of Absolute Differences (SAD) based Mosaic Algorithm. The use of SAD based Mosaic Algorithm in an unoptimised way increases the hardware requirements.…”

mentioning

confidence: 99%

FPGACam: A FPGA based efficient camera interfacing architecture for real time video processing

Sarkar

Bhairannawar

Raja

2021

IET Circuits, Devices & Syst

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In most of the real time video processing applications, cameras are used to capture live video with embedded systems/Field Programmable Gate Arrays (FPGAs) to process and convert it into the suitable format supported by display devices. In such cases, the interface between the camera and display device plays a vital role with respect to the quality of the captured and displayed video, respectively. In this paper, we propose an efficient FPGA-based low cost Complementary Metal Oxide Semiconductor (CMOS) camera interfacing architecture for live video streaming and processing applications. The novelty of our work is the design of optimised architectures for Controllers, Converters, and several interfacing blocks to extract and process the video frames in real time efficiently. The flexibility of parallelism has been exploited in the design for Image Capture and Video Graphics Array (VGA) Generator blocks. The Display Data Channel Conversion block required for VGA to High Definition Multimedia Interface Conversion has been modified to suit our objective by using optimised Finite State Machine and Transition Minimiszed Differential Signalling Encoder through the use of simple logic architectures, respectively. The hardware utilization of the entire architecture is compared with the existing one which shows that the proposed architecture requires nearly 44% less hardware resources than the existing one.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…2) Depth Image Analysis: Fig.5 shows the remarkable images obtained by the designed system. In order to observe the range data more clearly, we display the value as the pixel data, namely, Pi xel Data = d/d u • 2 24 , where d u is the unambiguous range (6.25 m in this figure), and d is the measurement distance. In the right side of the range image is the legend whose pixel data represent the corresponding depth.…”

Section: A System Setupmentioning

confidence: 99%

“…For example, ARM-based platform can only work at around ten frames per second in DME635-Evalkit (ESPROS Photonics Corporation). Thanks to its parallel computational ability [24], FPGA platform has excellent performance compared with other embedded platforms. A real-time FPGA platform for ToF ranging needs to acquire raw images, determine phase shift, and calculate the distance between the objects and sensor [25].…”

mentioning

confidence: 99%

Efficient and Fast Implementation of Embedded Time-of-Flight Ranging System Based on FPGAs

et al. 2017

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Time-of-flight cameras perceive depth information about the surrounding environment with an amplitudemodulated near-infrared light source. The distance between the sensor and objects is calculated through measuring the time the light needs to travel. To be used in fast and embedded applications, such as 3-D reconstruction, visual SLAM, humanrobot interactions, and object detection, the 3-D imaging must be performed at high frame rates and accuracy. Thus, this paper presents a real-time field programmable gate arrays platform that calculates the phase shift and then the distance. Experimental results shown that the platform can acquire ranging images at the maximum frame rate of 131 fps with a fine measurement precision (appropriately 5.1 mm range error at 1.2 m distance with the proper integration time). Low resource utilization and power consumption of the proposed system make it very suitable for embedded applications.

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Real-time implementation of panoramic mosaic camera based on FPGA

Cited by 9 publications

References 15 publications

Visual driving assistance system based on few-shot learning

Visual driving assistance system based on few-shot learning

FPGACam: A FPGA based efficient camera interfacing architecture for real time video processing

Efficient and Fast Implementation of Embedded Time-of-Flight Ranging System Based on FPGAs

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