Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Velazquez-Pupo, Roxana; Sierra-Romero, Alberto; Torres-Roman, D.; Shkvarko, Yuriy V.; Santiago-Paz, Jayro; Gómez‐Gutiérrez, David; Robles-Valdez, Daniel; Hermosillo-Reynoso, Fernando; Romero-Delgado, Misael

doi:10.3390/s18020374

Cited by 56 publications

(31 citation statements)

References 52 publications

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“…Notwithstanding considerable improvements in image processing techniques, available commercial software are dogged by problems of inability to detect and handle vehicle occlusions from camera view [28]- [30], limited functionality in severe weather conditions [31], [32], undesired factors like damaged road or white marks on the road surface and shadows of trees and buildings and nighttime vehicle detection [33], and overcrowded roads [34]. Another major drawback of video analytic algorithms is the lack of inter-system compatibility with already installed hardware, unless these two components are products of the same manufacturer.…”

Section: Developments In Image Processing Methodsmentioning

confidence: 99%

Image Processing in Intelligent Traffic Management

2019

ijrte

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Traffic monitoring and traffic control have always been challenging tasks. Intelligent Transportation Systems (ITS) based on wide range of technologies have certain practical challenges in their application and implementation. Video surveillance has proven advantageous over traditional systems based on inductive loops sensors and detectors for traffic monitoring. Accurate traffic density estimation which is basic to tackling traffic congestions requires detection of vehicles, assessing their speed, and tracking vehicles passing through surveillance zones. Image processing techniques require processing of large number of image frames for real-time applications in traffic management. More efficient and less costly image processing techniques for accurate vehicle detection and density determination are required for developing more effective traffic management systems. There is a need for developing algorithms with robust performance under heavy traffic loads and varied environmental conditions. Developments in artificial intelligence offer new vistas in image processing for regulation and management of traffic by signal control mechanisms and creation of neural networks for unhindered traffic flow.

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Section: Developments In Image Processing Methodsmentioning

confidence: 99%

Image Processing in Intelligent Traffic Management

2019

ijrte

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“…A simple background image estimation is performed to separate moving objects in foreground from the background, which can be modeled by [23], e.g., the statistical mode of each x t (i, j) at which the Probability Mass Function (pmf) takes its maximum.…”

Section: Background Image Estimationmentioning

confidence: 99%

“…L c (n, (m)) = {(r i (m) , r j (m+1) ), (r j (m+1) , r p (m+2) )} (23) or by the ordered set of the corresponding locations:…”

mentioning

confidence: 99%

A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Hermosillo-Reynoso

Torres-Roman

Santiago-Paz

et al. 2018

Entropy

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Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ, its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value;(2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

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“…However, using hyperplane recognition model, SVM can't accurately classify the samples with nonuniform state distribution. In addition, SVM is restricted in practical application for its inherent binary classification properties [25].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

Shang

Feng

2019

Entropy

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Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.

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Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Cited by 56 publications

References 52 publications

Image Processing in Intelligent Traffic Management

Image Processing in Intelligent Traffic Management

A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

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