Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection

Object detection generally requires sliding-window classifiers in tradition or anchor box based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in boxes. In this paper, we provide a new perspective where detecting objects is motivat...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-11
Main Authors: Liu, Wei, Irtiza Hasan, Liao, Shengcai
Format: Article
Language:English
Subjects:
Anchors
Computer vision
Convolution
Image analysis
Levels
Machine learning
Mathematical models
Object recognition
Pedestrians
Semantics
Sensors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Object detection generally requires sliding-window classifiers in tradition or anchor box based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in boxes. In this paper, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are objects, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the central points, which is also a straightforward convolution. Therefore, in this paper, pedestrian and face detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys a box-free setting. Though structurally simple, it presents competitive accuracy on several challenging benchmarks, including pedestrian detection and face detection. Furthermore, a cross-dataset evaluation is performed, demonstrating a superior generalization ability of the proposed method. Code and models can be accessed at (https://github.com/liuwei16/CSP and https://github.com/hasanirtiza/Pedestron).
ISSN:2331-8422