# Pedestrian Detection aided by Deep Learning Semantic Tasks

arXiv: https://arxiv.org/abs/1412.0069 · HTML: https://mlsanity.com/paper/1412.0069/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2014.

## Built on by (most cited first, 16 of 16)

- [UniHCP: A Unified Model for Human-Centric Perceptions](https://mlsanity.com/paper/2303.02936/) (2023)
- [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021)
- [Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion](https://mlsanity.com/paper/2312.10692/) (2023)
- [You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception](https://mlsanity.com/paper/2312.05525/) (2023)
- [Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization](https://mlsanity.com/paper/2106.06485/) (2021)
- [Robust pedestrian detection in thermal imagery using synthesized images](https://mlsanity.com/paper/2102.02005/) (2021)
- [Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2404.17929/) (2024)
- [Feature Calibration Network for Occluded Pedestrian Detection](https://mlsanity.com/paper/2212.05717/) (2022)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection](https://mlsanity.com/paper/2305.10801/) (2023)
- [Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning](https://mlsanity.com/paper/2308.13503/) (2023)
- [VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models](https://mlsanity.com/paper/2408.13031/) (2024)
- [Partially fake it till you make it: mixing real and fake thermal images for improved object detection](https://mlsanity.com/paper/2106.13603/) (2021)
- [A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images](https://mlsanity.com/paper/2209.02190/) (2022)
- [SNN-PAR: Energy Efficient Pedestrian Attribute Recognition via Spiking Neural Networks](https://mlsanity.com/paper/2410.07857/) (2024)
- [The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications](https://mlsanity.com/paper/2411.19714/) (2024)

## Abstract (arXiv)

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.