A Brief Survey on Person Recognition at a Distance
- URL: http://arxiv.org/abs/2212.08969v1
- Date: Sat, 17 Dec 2022 22:15:10 GMT
- Title: A Brief Survey on Person Recognition at a Distance
- Authors: Chrisopher B. Nalty, Neehar Peri, Joshua Gleason, Carlos D. Castillo,
Shuowen Hu, Thirimachos Bourlai, Rama Chellappa
- Abstract summary: Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras.
Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging.
- Score: 46.47338660858037
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Person recognition at a distance entails recognizing the identity of an
individual appearing in images or videos collected by long-range imaging
systems such as drones or surveillance cameras. Despite recent advances in deep
convolutional neural networks (DCNNs), this remains challenging. Images or
videos collected by long-range cameras often suffer from atmospheric
turbulence, blur, low-resolution, unconstrained poses, and poor illumination.
In this paper, we provide a brief survey of recent advances in person
recognition at a distance. In particular, we review recent work in
multi-spectral face verification, person re-identification, and gait-based
analysis techniques. Furthermore, we discuss the merits and drawbacks of
existing approaches and identify important, yet under explored challenges for
deploying remote person recognition systems in-the-wild.
Related papers
- Multimodal Adaptive Fusion of Face and Gait Features using Keyless
attention based Deep Neural Networks for Human Identification [67.64124512185087]
Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification.
We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
arXiv Detail & Related papers (2023-03-24T05:28:35Z) - Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks [55.81577205593956]
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously.
Deep learning (DL) has been brought to this emerging field and inspired active research endeavors in mining its potential.
arXiv Detail & Related papers (2023-02-17T14:19:28Z) - Video-based Human Action Recognition using Deep Learning: A Review [4.976815699476327]
Human action recognition is an important application domain in computer vision.
Deep learning has been given particular attention by the computer vision community.
This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques.
arXiv Detail & Related papers (2022-08-07T17:12:12Z) - A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets
and Challenges [18.08349977960643]
Gait recognition aims to identify a person at a distance, serving as a promising solution for long-distance and less-cooperation pedestrian recognition.
Recent advancements in gait recognition have achieved inspiring success in many challenging scenarios by utilizing deep learning techniques.
New challenges for gait recognition, including robust deep representation modeling, in-the-wild gait recognition, and recognition from new visual sensors such as infrared and depth cameras.
arXiv Detail & Related papers (2022-06-28T03:36:12Z) - RealGait: Gait Recognition for Person Re-Identification [79.67088297584762]
We construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner.
Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
arXiv Detail & Related papers (2022-01-13T06:30:56Z) - The State of Aerial Surveillance: A Survey [62.198765910573556]
This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective.
The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
arXiv Detail & Related papers (2022-01-09T20:13:27Z) - Evaluation of Human and Machine Face Detection using a Novel Distinctive
Human Appearance Dataset [0.76146285961466]
We evaluate current state-of-the-art face-detection models in their ability to detect faces in images.
The evaluation results show that face-detection algorithms do not generalize well to diverse appearances.
arXiv Detail & Related papers (2021-11-01T02:20:40Z) - Differential Anomaly Detection for Facial Images [15.54185745912878]
Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation.
Most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time.
We introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images.
arXiv Detail & Related papers (2021-10-07T13:45:13Z) - Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis [69.09526348527203]
Deep generative models have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes.
We propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection.
We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios.
arXiv Detail & Related papers (2021-05-29T21:22:24Z) - Survey on Reliable Deep Learning-Based Person Re-Identification Models:
Are We There Yet? [19.23187114221822]
Person re-identification (PReID) is one of the most critical problems in intelligent video-surveillance (IVS)
Deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time.
We present descriptions of each model along with their evaluation on a set of benchmark datasets.
arXiv Detail & Related papers (2020-04-30T16:09:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.