Pedestrian Detection Directing at the Region of Interest in Videos

2012 ◽  
Author(s):  
Hongmei Li ◽  
Rentao Gu ◽  
Qing Ye ◽  
Yuefeng Ji
Keyword(s):  
Pedestrian Detection ◽  
Region Of Interest

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

scholarly journals A real-time pedestrian detection system for safety applications

2021 ◽  
Author(s):  
Rinju Alice John
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Model Method ◽  
System A ◽  
And Performance ◽  
Performance Rate ◽  
Safety Applications

Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

scholarly journals Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1089 ◽  
Author(s):  
Ye Wang ◽  
Zhenyi Liu ◽  
Weiwen Deng
Keyword(s):  
Pedestrian Detection ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Fixed Size ◽  
Feature Maps ◽  
Feature Map ◽  
Bounding Box ◽  
New Feature ◽  
And Training

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.

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