A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers

Min, Weidong; Fan, Mengdan; Guo, Xiaoguang; Han, Qing

doi:10.1109/tits.2017.2756989

Cited by 67 publications

(28 citation statements)

References 42 publications

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“…The SVM algorithm, originated from statistical learning theory, is an intelligent machine learning (ML) algorithm with very good generalization, which has been widely used in system classification, regression analysis, system state recognition, and other related fields [15]. The SVM algorithm has excellent performance and strong generalization ability.…”

Section: The Convolutional Neural Networkmentioning

confidence: 99%

Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

Sang

Sun

2020

THERM SCI

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The paper aims to study the identification and diagnosis of infrared thermal fault of airborne circuit board of equipment, expand the application of intelligent algorithm in infrared thermal fault diagnosis, and promote the development of computer image processing technology and neural network technology in the field of thermal diagnosis. Taking the airborne circuit board in the boiler plant as the research object, first, the sequential analysis method was selected to collect the temperature changes during the operation of the circuit board. Second, on the basis of convolutional neural network, the program was written in Python, and the Relu function was used as the activation function establish the thermal fault diagnosis method of the on-board circuit board of the boiler plant equipment based on the convolutional neural network model. Third, based on the support vector machine intelligent algorithm, genetic algorithm was used to optimize the parameters, and combined with the grey prediction model, the infrared thermal fault diagnosis scheme of the circuit board of the multistage support vector machine boiler plant equipment was constructed. The results showed that the accuracy of the model after 6000 iterations was stable between 0.92-0.96, and the loss function value was stable at about 0.17. After the optimization of genetic algorithm, the accuracy of thermal fault diagnosis based on support vector machine model was optimized. Compared with grey prediction model, the accuracy of support vector machine model for fault diagnosis was higher, mean square error value was 0.0258, and the correlation coefficient was 91.55%. To sum up, the support vector machin model shows higher accuracy than grey prediction model, which can be used for thermal fault diagnosis.

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Section: The Convolutional Neural Networkmentioning

confidence: 99%

Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

Sang

Sun

2020

THERM SCI

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“…In recent years, convolutional neural networks (CNNs) have dramatically improved the states of the art in image object recognition [10] [11], image classification [12], and image scene analysis [13] [14]. Inspired by this success, various CBRSIR methods that are based on CNNs have been put forward and seem to be becoming more popular than SIFT-based models.…”

Section: Introductionmentioning

confidence: 99%

Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

Zhao

Luo

et al. 2020

IEEE Access

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Many image features have been proposed for image retrieval; hence, effectively fusing these features to alleviate the large variation in performance among image queries when using single image features has become a major challenge in remote sensing (RS) image retrieval. Because high-resolution remote sensing images have abundant and complex visual contents, accurately measuring the similarity between two images is another important problem. To address these challenges, we propose a novel RS image retrieval method that uses query-adaptive feature weights to fuse features and utilizes two image similarities to improve retrieval performance. First, we use the image rank similarity, which measures the similarity between two images according to their corresponding top-m image lists from a reference image collection, to calculate the similarity of each feature between a query image and each retrieved image. Then, we assign a weight to each feature to fuse these features via our query-adaptive weighting method. Finally, we take the query image and its neighborhood set selected from the retrieval dataset as the query class and utilize the image-to-query class similarity to re-rank the retrieval results. Extensive experiments are conducted on two publicly available RS image databases. Compared with the state-of-the-art methods, the proposed method can significantly enhance the retrieval precision.

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“…Raw tracklets are considered as sample points and are grouped to build different vehicle candidates. Min et al [24] introduced an approach for tracking multiple vehicles. In this method, they used an improved ViBe algorithm and the gray-scale spatial information for accurate vehicle detection.…”

Section: Introductionmentioning

confidence: 99%

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

Delavarian

Marouzi

Hassanpour

2020

IJE

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Visual vehicle tracking is an important topic in intelligent transportation systems. Intersections are challenging locations for visual systems to track vehicles which are simultaneously moving in different directions. In addition, normal traffic flow may change at intersections due to accidents. Congestion, occlusion and undetermined motion flows are the nominated challenging issues of vehicle tracking at intersections. In this paper, a method for tracking multiple vehicles is proposed considering the vehicle motion directions to overcome undetermined motion flows. For this purpose, a multilayer model is presented, which assigns each motion flows to distinct layers. Moreover, we introduce different neighborhoods for various layers considering the regular motion flows in a layer. Hence, vehicles entering from the same side of intersection with the same motion direction are assigned to the same layer. Then the tracking is performed on different layers separately. In special cases such as vehicles crossing each other, misdetections or occlusion, the proposed tracking method can predict the vehicles tracks by using the stored tracking history and considering neighborhoods in that layer. Experimental results show consistency between proposed tracking method results and ground truth, also outperformance of other tracking methods in tracking vehicles crossing the intersection.

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A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers

Cited by 67 publications

References 42 publications

Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

Thermal energy diagnosis of boiler plant by computer image processing and neural network technology

Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

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