A fast pedestrian detection algorithm based on feature pyramid(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2018年05期
- Page:
- 231-237
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- A fast pedestrian detection algorithm based on feature pyramid
- Author(s):
- WANG Shifang; XU Kun; CHEN Mingyao
- Keywords:
- traffic information and control engineering; pedestrian detection; feature pyramid; channel feature; decision tree
- PACS:
- -
- DOI:
- -
- Abstract:
- Amid at improving accuracy and speed of pedestrian detection， in the presence of background changes, measure uncertainty in pedestrian scale and occlusion was measured，based on the characteristics of the channel feature can be predicted reliably across adjacent scales， Therefore, a fast pedestrian detection method based on feature pyramid was proposed. First, channel features were aggregated at every key scale, which was composed of three LUV color space channels，one normalized gradient magnitude and six gradient direction histograms were calculated， which can fully reflected gradient and color information of the image. Second, based on the predictability of channel characteristics between adjacent scales， multiscale features of adjacent scales were estimated, and the feature pyramid was constructed quickly and efficiently. Then, with the bootstrapping framework, the AdaBoost algorithm was used to train secondorder decisiontrees to form a pedestrian classifier. Finally, during pedestrian detection, the aggregated channel features on every scale were segmented to obtain blocks, and the blocks were input to the trained cascade classifier. Window coordinates and scores of candidate windows were recorded. Nonmaximum suppression was used to screen the candidate windows. The final pedestrian detection box was output at last. The proposed algorithm was carried out on the ETH and TUD public datasets, and the results were compared with the HOG, VJ, and DPM methods. The results show that the proposed method and the DPM method have higher detection accuracies than the VJ and HOG methods. The performance of the method proposed in this paper is superior to the DPM method, regarding missed detections, false detections, and window positioning accuracy when the angle of view changes and in the presence of pedestrian occlusion. At the same time, 〖JP2〗the frame rate of the proposed method reaches 29 frames per second, which greatly improves detection speed and is able to meet realtime detection requirement. 1 tabs, 9 figs, 21 refs.
Last Update: 2018-10-23