Real-time omnidirectional image sensor (COPIS) for vision-guided navigation

1994 ◽  
Vol 10 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Y. Yagi ◽  
S. Kawato ◽  
S. Tsuji
Keyword(s):  
Real Time ◽  
Image Sensor ◽  
Omnidirectional Image

scholarly journals In-Situ Real-Time Focus Detection during Laser Processing Using Double-Hole Masks and Advanced Image Sensor Software

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1540 ◽  
Author(s):  
Binh Cao ◽  
Phuong Hoang ◽  
Sanghoon Ahn ◽  
Jeng-o Kim ◽  
Heeshin Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Laser Processing ◽  
Image Sensor ◽  
Focus Detection

scholarly journals Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1174 ◽  
Author(s):  
Jian Luo ◽  
Chang Lin
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sensor ◽  
Frame Rate ◽  
External Memory ◽  
Support Vector ◽  
Svm Classification ◽  
Prior Art ◽  
Memory Modules

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

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