scholarly journals Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yingfeng Cai ◽  
Ze Liu ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
Hai Wang ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
Back Propagation ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Good Efficiency ◽  
Safe Driving ◽  
Deep Model ◽  
Deformable Part Model ◽  
Driving Assistance ◽  
Part Model

Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.

scholarly journals Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Wenhui Li ◽  
Peixun Liu ◽  
Ying Wang ◽  
Hongyin Ni
Keyword(s):  
Similarity Measure ◽  
Choquet Integral ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Collision Warning ◽  
Feature Similarity ◽  
Driving Assistance ◽  
Forward Collision Warning

Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

scholarly journals A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4109 ◽  
Author(s):  
Hai Wang ◽  
Yijie Yu ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Xiaobo Chen
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Restricted Boltzmann Machines ◽  
Deep Model ◽  
Multiple Feature ◽  
Art Methods ◽  
Feature Subspace

Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.

scholarly journals Vehicle detection algorithm based on LW-SSD

2021 ◽  
Vol 1748 ◽  
pp. 032042
Author(s):  
Yunxiang Liu ◽  
Guoqing Zhang
Keyword(s):  
Vehicle Detection ◽  
Detection Algorithm
Sign in / Sign up

Export Citation Format

Share Document