Abstract | ||
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Pedestrian detection plays an important role in many applications. Since its birth 13 years ago, Histogram Of Gradient (HOG) descriptor has become a popular descriptor for pedestrian detection. Besides its original instantiation, the HOG also reflects a general methodology of constructing descriptors based on histograms of gradients of certain image sub-blocks. Following this general methodology, a number of HOG-style descriptors have been reported in the literature. The generation process of these descriptors is summarized in this work, and a new descriptor is presented for pedestrian detection. Three contributions are made in this work. First, a general model called descriptor generation model (DGM) is proposed, which can be used to systematically construct a wide range of HOG-style descriptors for pedestrian detection. Second, based on the DGM, a pedestrian detection experimental framework (PDEF) is introduced to find the optimal HOG-style descriptor. In the PDEF, the performance of each descriptor can be evaluated. At last, the genetic algorithm is employed to search the optimal (or semi-optimal) HOG-style descriptor in the descriptor space. And a new descriptor named Second-order Gradient for Pedestrian detection (G2P) is presented. Experimental results demonstrate the advantage of the G2P descriptor over the standard HOG descriptor with ETH, CVC-02-system, NITCA and KITTI dataset, which also reflects the effectiveness of the DGM-based PDEF in finding better descriptors for pedestrian detection. |
Year | DOI | Venue |
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2017 | 10.1007/s00521-018-3815-4 | SPAC |
Keywords | Field | DocType |
Pedestrian detection, Descriptor, G2P, Computer vision | Kernel (linear algebra),Histogram,Pattern recognition,Pattern analysis,Automation,Artificial intelligence,Pedestrian detection,Genetic algorithm,Cybernetics,Mathematics,Machine learning | Conference |
Volume | Issue | ISSN |
32 | 9 | 1433-3058 |
ISBN | Citations | PageRank |
978-1-5386-3017-4 | 1 | 0.35 |
References | Authors | |
19 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Ming Yang | 1 | 91 | 30.46 |
Yeqiang Qian | 2 | 5 | 2.88 |
Linji Xue | 3 | 1 | 0.35 |
Hao Li | 4 | 261 | 85.92 |
liuyuan deng | 5 | 9 | 1.81 |
C. Wang | 6 | 14 | 5.86 |