A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles

2015 ◽  
Author(s):  
Xiaofeng Han ◽  
Jianfeng Lu ◽  
Ying Tai ◽  
Chunxia Zhao
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles

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.

scholarly journals Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

2022 ◽  
Vol 12 (2) ◽  
pp. 682
Author(s):  
Yuzhan Wu ◽  
Chenlong Li ◽  
Changshun Yuan ◽  
Meng Li ◽  
Hao Li
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Predictive Control ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
The Stability

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

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