An Approach to Detect Vehicles in Multiple Climatic Conditions Using the Corner Point Approach

Anandhalli, Mallikarjun; Baligar, Vishwanath P.

doi:10.1515/jisys-2016-0073

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…Each traffic image in the convolution neural network determines these regions after applying graphical and parallel detection and recognition by utilizing efficient and parallel pre-processing means. More recent research related to the motivation of the manuscript can be found in references [ 21 , 22 ].…”

Section: The Related Workmentioning

confidence: 99%

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials

Xie¹,

Shi²,

Wang³

2022

Applied Bionics and Biomechanics

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Traffic indication is an important part of the road environment, providing information about road conditions, restrictions, prohibitions, warnings, and the current status related to the flow of the traffic and other navigational aspects. The shape, color, and pictogram of a traffic indication are encoded into the visual characteristics of traffic signs. Not paying attention to these traffic signs could lead directly or indirectly to traffic accidents. In this article, the support traffic indication vector recognition (STIVR) method is proposed to classify the best signal detection to avoid traffic congestion and accidents. The proposed STIVR recognizes the traffic indication system automatically, reduces occurrences of traffic accidents, and helps drivers move safely on different pavement materials. Besides, the adaptive median filter (AMF) algorithm is used to pre-process and protect the traffic indication images without obscuring them. Thus, it indicates the edge of the non-smoothed nasty ferment from the service. In the detection of traffic events, indication images are enhanced, pre-treated, and divided according to symbols and their characteristics such as color, shape, or both. The output becomes a segmented image, including the available space identified as a road sign. The experimental results show that the proposed method functions well; achieves a sufficiently higher process speed and better segmentation of traffic indications and more accuracy in recognition of the objects. For example, the proposed method reaches a higher sensitivity performance of 96%.

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Section: The Related Workmentioning

confidence: 99%

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials

Xie¹,

Shi²,

Wang³

2022

Applied Bionics and Biomechanics

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“…To detect triangular and rectangular symbols, Anandhalli and Baligar [15] applied the Harris corner detector to the ROIs, searched for corners in the predefined control area. Li [16] relied on edge information to recognize traffic signs that are difficult to detect in the driving environment: Based on the shape features of scale-invariant edge turning angles, the nonparametric shape detector was used to detect circles, triangles, and rectangles in the image; more than 95% of all traffic signs were covered by this detector.…”

Section: A Shape-based Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.

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“…Once the detected vehicles are verified then the centroid of the object or the interest points of the objects are passed to the CNN. The CNN will help in reliable tracking of the vehicles even though the occlusion, shadows appear in the image [23][24].…”

Section: Corner Pointsmentioning

confidence: 99%

“…To enhance the performance, CNN is utilized for classification and recognition of the image. CNN includes lakhs of different images of different class [10][11]. CNN is used to extract information of image and to study complicated features.…”

mentioning

confidence: 99%

Vehicle detection and tracking for traffic management

Anandhalli¹,

Baligar

Baligar³

et al. 2021

IJ-AI

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<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

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An Approach to Detect Vehicles in Multiple Climatic Conditions Using the Corner Point Approach

Cited by 7 publications

References 6 publications

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials

Automatic Recognition of Traffic Signs Based on Visual Inspection

Vehicle detection and tracking for traffic management

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