Pedestrian Detection aided by Deep Learning Semantic Tasks

CVPR 2015 Yonglong TianPing LuoXiaogang WangXiaoou Tang

Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they mainly capture middle-level representations, such as pose of pedestrian, but confuse positive with hard negative samples, which have large ambiguity, e.g. the shape and appearance of `tree trunk' or `wire pole' are similar to pedestrian in certain viewpoint... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Pedestrian Detection Caltech TA-CNN Reasonable Miss Rate 20.9 # 21

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