Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

Label assignment has been widely studied in general object detection because of its great impact on detectors' performance. However, none of these works focus on label assignment in dense pedestrian detection. In this paper, we propose a simple yet effective assigning strategy called Loss-aware Label Assignment (LLA) to boost the performance of pedestrian detectors in crowd scenarios. LLA first calculates classification (cls) and regression (reg) losses between each anchor and ground-truth (GT) pair. A joint loss is then defined as the weighted summation of cls and reg losses as the assigning …

一种用于航空图像小目标检测的数据驱动RetinaNet模型

A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images

本研究针对航空图像小目标检测需求，提出数据驱动的DDR-Net模型，可自主确定最优特征图与锚框，搭配创新采样技术提升有限数据下的性能，实验证明其效果优于RetinaNet等现有模型，可支撑多领域应用。

面向自动驾驶行人检测的安全自适应损失函数

A Safety-Adapted Loss for Pedestrian Detection in Automated Driving

本文提出一种安全自适应损失函数，利用行人关键性分数（基于TTC-RSB和距离）训练检测器，在nuScenes上验证能减少关键行人漏检而不损整体性能。

TJU-DHD: A Diverse High-Resolution Dataset for Object Detection

Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems. Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on the specific scenarios. Moreover, the popular…

Efficient Object Detection Model for Real-Time UAV Applications

Unmanned Aerial Vehicles (UAVs) especially drones, equipped with vision techniques have become very popular in recent years, with their extensive use in wide range of applications. Many of these applications require use of computer vision techniques, particularly object detection from the information captured by on-board camera. In this paper, we propose an end to end object detection model running on a UAV platform which is suitable for real-time applications. We propose a deep feature pyramid architecture which makes use of inherent properties of features extracted from Convolutional Network…

WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: 1) most of existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity; 2) crowd scenarios with highly occluded pedestrians are still under represented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse datase…