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In recent years, the demand for highly accurate pedestrian detectors has increased due to the development of advanced driving support systems. For the training of an accurate pedestrian detector, it is important to collect a large number of training samples. To support this, this paper proposes a “hard negative” mining method to automatically extract background images which tend to be erroneously detected as pedestrians. Negative samples are selected based on the assumption that frequent patterns observed multiple times in the same location are most likely parts of the background scene. As a result of an evaluation using in-vehicle camera images captured along the same route, we confirmed that the proposed method can automatically collect false positive samples accurately. We also confirmed that a highly accurate detector can be constructed using the additional negative samples.

Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns