Pedestrian Detection aided by Deep Learning Semantic Tasks

by   Yonglong Tian, et al.

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. This ambiguity can be distinguished by high-level representation. To this end, this work jointly optimizes pedestrian detection with semantic tasks, including pedestrian attributes (e.g. `carrying backpack') and scene attributes (e.g. `road', `tree', and `horizontal'). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task objective function is carefully designed to coordinate tasks and reduce discrepancies among datasets. The importance coefficients of tasks and network parameters in this objective function can be iteratively estimated. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech and ETH datasets, where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively.


page 1

page 4

page 12

page 13

page 14


Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in r...

Temporal Attribute-Appearance Learning Network for Video-based Person Re-Identification

Video-based person re-identification aims to match a specific pedestrian...

SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection

Pedestrian detection plays an important role in many applications such a...

High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection

Object detection generally requires sliding-window classifiers in tradit...

Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization

State-of-the-art methods treat pedestrian attribute recognition as a mul...

Learning Deep Representation for Face Alignment with Auxiliary Attributes

In this study, we show that landmark detection or face alignment task is...

Hierarchically Robust Representation Learning

With the tremendous success of deep learning in visual tasks, the repres...

Please sign up or login with your details

Forgot password? Click here to reset