Human detection from images and videos: A survey

By Dinh C. Nguyen, Wanqing Li, Philip O. Ogunbona. 2016

267 citations recorded by the source corpus.

The problem of human detection is to automatically locate people in an image or video sequence and has been actively researched in the past decade. This paper aims to provide a comprehensive survey on the recent development and challenges of human detection. Different from previous surveys, this survey is organised in the thread of human object descriptors. This approach has advantages in providing a thorough analysis of the state-of-the-art human detection methods and a guide to the selection of appropriate methods in practical applications. In addition, challenges such as occlusion and real-time human detection are analysed. The commonly used evaluation of human detection methods such as the datasets, tools, and performance measures are presented and future research directions are highlighted. HighlightsA review on the state-of-the-art of human detection.This review is organised in the thread of human object descriptors.Challenges such as occlusion and real-time human detection are analysed.The commonly used datasets, tools, and performance measures are presented.Open issues and future research directions are highlighted.A guide to the selection of detection methods for applications is provided.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jianfei Cai
2. G. Wang
3. Jiuxiang Gu
4. Amir Shahroudy
5. Tsuhan Chen
6. Sergio Escalera
7. Bing Shuai
8. Gang Wang

In Electrical and Electronic Engineering

1. Jianfei Cai
2. Jiuxiang Gu
3. Mohammad Sabokrou
4. Tsuhan Chen

Citations in the corpus

• Recent advances in convolutional neural networks (2015, background): “Recently, object proposal based methods attract a lot of interests and are widely studied in the literature [185, 193, 200, 201].”
• Survey on Emotional Body Gesture Recognition (2018, background): “If depth information is available, it can be used to limit the search space and considerably simplify the background substraction problem [54].”
• Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection (2018, background): “Pedestrian detection, as a canonical sub-problem of general object detection, has been intensively investigated by the computer vision community [1, 2, 3], due to its diversified applications in video surveillance, car safety, image retrieval, robotics, etc.”
• Facial Expression Analysis under Partial Occlusion (2018, methodology): “ Overviews of face recognition or human detection methods under partial occlusion [Azeem et al. 2014], [Nguyen et al. 2016].”
• Vision-based human activity recognition: a survey (2020, background): “Overviews of current progress in human activities recognition are also presented in [7, 21, 122, 174].”