Name
Title | ||
|---|---|---|
Research on Improved Pedestrian Detection Algorithm Based on Convolutional Neural Network |
Abstract | ||
|---|---|---|
As we all know, The feature obtained by the convolutional neural network (CNN) is the expression of higher level for the input image, which can improve the discrimination of the input data. In this paper, we use an excellent convolutional neural network framework to design the pedestrian detection algorithm. Aiming at the background interference between the descendant and the occlusion between the pedestrians, the head of pedestrian is considered as the detection object, so the features of the object to be tested are reduced. To obtain a better detection effect and accelerate the calculation speed, the regional proposal network (RPN) in fast RCNN can be used as a better pedestrian detector, but the latter classifier reduces the performance. The object detection network is improved based on fast RCNN, and the features of the first layer are merged, so that the obtained features are richer. Experiments show that the new method can separate the objects from the background accurately. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00063 | 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) |
Keywords | Field | DocType |
CNN,Pedestrian detection,Feature extraction | Object detection,Convolutional neural network,Computer science,Algorithm,Feature extraction,Artificial intelligence,Deep learning,Classifier (linguistics),Hidden Markov model,Detector,Pedestrian detection | Conference |
ISBN | Citations | PageRank |
978-1-7281-2981-5 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiachi Wang | 1 | 0 | 0.34 |
| Hang Li | 2 | 38 | 21.94 |
| Shoulin Yin | 3 | 6 | 5.87 |
| Yang Sun | 4 | 46 | 15.21 |