Traffic density estimation via a multi-level feature fusion network

Author(s):  
Ying-Xiang Hu ◽  
Rui-Sheng Jia ◽  
Yong-Chao Li ◽  
Qi Zhang ◽  
Hong-Mei Sun
2019 ◽  
Vol 55 (13) ◽  
pp. 742-745 ◽  
Author(s):  
Kang Yang ◽  
Huihui Song ◽  
Kaihua Zhang ◽  
Jiaqing Fan

Author(s):  
Devashish Prasad ◽  
Kshitij Kapadni ◽  
Ayan Gadpal ◽  
Manish Visave ◽  
Kavita Sultanpure

2021 ◽  
Vol 191 ◽  
pp. 106479
Author(s):  
Qixin Sun ◽  
Xiujuan Chai ◽  
Zhikang Zeng ◽  
Guomin Zhou ◽  
Tan Sun

2019 ◽  
Vol 110 ◽  
pp. 176-184 ◽  
Author(s):  
Debojit Biswas ◽  
Hongbo Su ◽  
Chengyi Wang ◽  
Aleksandar Stevanovic ◽  
Weimin Wang

Author(s):  
Arjun Benagatte Channegowda ◽  
H N Prakash

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.


Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.


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