Abstract：In the existing pedestrian detection algorithms, the pedestrian detection is considered as a supervised learning problem of two classes, pedestrian and background. Thus, the pedestrian and the background in the video are distinguished. However, the problem of variable poses and heavy occlusion can not be solved by these algorithms effectively. In this paper, a pedestrian detection algorithm based on graph cuts and density clustering is proposed. The pedestrian detection is regarded as an unsupervised learning problem of multiple classes. At the training stage, the multilevel histogram of oriented gradient-local binary pattern(HOG-LBP) features are firstly calculated for each of training samples. Then, different weights are assigned to each image block of the multilevel HOG-LBP features. To distinguish the different parts of pedestrian and assign weight, the image sample is segmented by the block-based graph cuts algorithm. Finally, the density clustering approach is used to classify the positive and negative samples into multiple cluster center respectively. At the testing stage, the distance between the multilevel HOG-LBP of test sample and every cluster center is calculated, and the five shortest distances are voted to classify the test sample. Experiments show that the proposed algorithm can handle the pose variations and partial occlusions effectively. Moreover, with the increase of training samples, the results of the proposed algorithm can be comparable to that of the state-of-the-art pedestrian detection algorithms.
曾成斌，刘继乾. 基于图切割和密度聚类的视频行人检测算法*[J]. 模式识别与人工智能, 2017, 30(7): 588-587.
ZENG Chengbin, LIU Jiqian. Pedestrian Detection on Videos Based on Graph Cuts and Density Clustering. , 2017, 30(7): 588-587.
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