All-day traffic states recognition system without vehicle segmentation

Bi, Shuping; Li-qun, Han; Zhong, Yixin; Wang, Xiaojie

doi:10.1016/s1005-8885(10)60131-8

Cited by 11 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the output layer, the neuron number is the same as the category number. The maximum number of training times was set to 1000, the goal of the training accuracy was set to 1, the number of hidden neurons was set according to the experimental formula and incremental attempt, and the learning rate is set dynamically according to the training accuracy [40].…”

Section: Classification Model Establishment and Evaluationmentioning

confidence: 99%

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Wei

Liu

et al. 2023

Processes

View full text Add to dashboard Cite

The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.

show abstract

Section: Classification Model Establishment and Evaluationmentioning

confidence: 99%

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Wei

Liu

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…The Gaussian distribution model has been applied to many areas including the traffic management system [5,8,11,25] and outlier detection [26], because of its simplicity and effectiveness. In this paper, the Gaussian distribution model is applied to traffic anomaly detection.…”

Section: Gaussian Distribution Modelmentioning

confidence: 99%

Traffic anomaly detection based on image descriptor in videos

Liu

Huang

2015

Multimed Tools Appl

View full text Add to dashboard Cite

The huge and ever growing volume of traffic video poses a compelling demand for efficient automatic detection of traffic anomaly. In this paper, a new traffic anomaly detection algorithm is introduced. It firstly divides a traffic video into several video cubes in temporal domain, and each video cube is divided into video blocks in spatial domain. Each image block of a video block is described using the local invariant features and the visual codebook approach. Based on the descriptor of the image block, we count the category number of the block (CNB) of a video block. Then, a Gaussian distribution model for estimating the probability of normal traffic with respect to the CNB is learned. The learned Gaussian distribution model is then used to detect the traffic anomaly from the test traffic video. Eventually, the results of all video blocks are fused to achieve the final decision. Experimental results show that the proposed algorithm performs better than two existing algorithms on both the intersection traffic videos and main road traffic videos.

show abstract

“…Tan apply edge texture feature in ROI to classify traffic flow density into four levels (empty, low, high and full) [1] . Bi proposed an algorithm of Gaussian group-based histogram (GBH) to build background image, and describe the amount of vehicle running on the road according to average intensity of background subtract image [2] . Li proposed a traffic state detection system which can estimate traffic flow speed and road space occupancy, and recognize three typical traffic states (congested, slow, and smooth) [3] .…”

Section: Introductionmentioning

confidence: 99%

Traffic States Detection based on Block Matching

Jiang¹,

Zhang²,

Wang³

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

View full text Add to dashboard Cite

Abstract-A traffic states detection method is proposed to represent the road traffic conditions accurately and reduce labor intensive monitoring work in this paper. The edge of image, which is named as edge characteristic, is applied to describe the number of vehicles running on the road, and the average density of vehicle edge pixels is used to substitute the road occupation ratio. Meanwhile block matching method is used for estimating the speed of traffic flow. According the extracted feature parameters, a classifier is designed to classify the traffic states in three categories (congested, slow, and smooth). The experimental results showed the proposed algorithm is robust.

show abstract

All-day traffic states recognition system without vehicle segmentation

Cited by 11 publications

References 13 publications

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Traffic anomaly detection based on image descriptor in videos

Traffic States Detection based on Block Matching

Contact Info

Product

Resources

About