Abstract: 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.

Qing Zhang. “Research on Automatic Detection Algorithm for Pedestrians on the Road Based on Image Processing Method”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.2 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140276

@article{Zhang2023,
title = {Research on Automatic Detection Algorithm for Pedestrians on the Road Based on Image Processing Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140276},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140276},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Qing Zhang}
}