Pedestrian Crossing Detection Based on HOG and SVM

Zhang, Yunzuo; Guo, Kaina; Guo, Wei; Zhang, Jiayu; Li, Yang

doi:10.32604/jcs.2021.017082

Cited by 3 publications

(2 citation statements)

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“…Subsequent simulation experimental results on VOC2007 and data_sub showed that the maximum value of mAP was 77% and the maximum www.ijacsa.thesai.org accuracy was 96.31%. Zhang et al [7] designed a pedestrian target detection algorithm based on the histogram of oriented gradient and support vector machine (SVM). They found that the algorithm greatly reduced the computational effort when feature extraction was performed only on candidate regions, thus improving the detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Detection Algorithm for Pedestrians on the Road Based on Image Processing Method

Zhang¹

2023

IJACSA

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Accurate detection of pedestrian targets can effectively improve the performance level of intelligent transportation and surveillance projects. In order to effectively enhance the accuracy of detecting pedestrian targets on the road, this paper first introduced the traditional pedestrian target detection algorithm, proposed the faster recurrent convolutional neural network (RCNN) algorithm to detect pedestrian targets, and improved it to make good use of the convolutional features at different scales. Finally, support vector machine (SVM), traditional Faster RCNN, and optimized Faster RCNN algorithms were compared by simulation experiments. The results showed that the optimized Faster RCNN algorithm had higher detection accuracy and recall rate, obtained a more accurate target localization frame, and detected faster than SVM and traditional Faster RCNN algorithms; the traditional Faster RCNN algorithm had higher detection accuracy and target frame localization accuracy than the SVM algorithm.

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Section: Introductionmentioning

confidence: 99%

Research on Automatic Detection Algorithm for Pedestrians on the Road Based on Image Processing Method

Zhang¹

2023

IJACSA

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“…II. LITERATURE REVIEW Zebra-crossings are detected by looking for groups of concurrent lines [1] where three methods are used for color detection and segmentation which includes RGB images being converted into IHLS color space and these methods are tested on outdoor images [2] and many other threshold image techniques such as Gaussian filter, Canny edge detection Contour, and Fit Ellipse [3][4] are used for traffic sign recognition with Kalman filter [5] which also includes Block-based Hough proposed by Yu-Quin Bao [6] and transform and directional variance techniques [7], a novel approach to detect and locate the zebra-crossings and the system is found out to be feasible for use on public roads around the world [8] to obtain 13,40 high-quality photo-realistic images from the video from 13 classes of various objects [9]. The design of a low-power, low-latency electronic mobility assistance for blind persons revealed that decision trees, random forests, and KNNs may all be used to recognise objects [10].…”

Section: Introductionmentioning

confidence: 99%

Zebra Crossing Detection Using Concatenation of SIFT-Gabor Features

Madake

2023

IJRASET

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Zebra-crossing recognition is a challenging activity for visually impaired navigating safely. This paper proposes a zebra-crossing scene recognition model-based fusion of texture features and geometrical features. The Gabor features for 8 orientations are extracted for different orientations Zebra Crossing lines. The SIFT is employed for keypoint detection and description. The final features vector is obtained by concatenating Gabor and SIFT features. The feature vector is optimized using K-means clustering and Principle Component Analysis (PCA). The optimized feature vectors were classified using five Classifiers, SVM, Random Forest, Decision Tree, Naïve Bayes Classifiers. The proposed model successfully recognizes the crosswalks with 89% accuracy. The different classifier models are evaluated for Precision, Accuracy, F1 Score, Recall, and RoC. This paper specifies the classification of two class positive which is zebra-crossing and negative which is no zebra-crossing. The designed system will classify either the crosswalk is detected or not on the basis of percentage specified for each class. This can be done by matching the features of the trained dataset. Those features are matched with the real videos or images to recognize the motion and classify into specific class. More specific crosswalk detection in real time can be executed to get more specific type of positive and negative class and classification.

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