Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling

Coşar, Serhan; Bellotto, Nicola

doi:10.1007/s10846-019-01026-w

Cited by 14 publications

(5 citation statements)

References 31 publications

(36 reference statements)

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“…In the past ten years, literature on pedestrian detection and tracking based on computer vision has become one of the most active international research topics in the field of pattern recognition, computer vision, and artificial intelligence [ 6 ]. The core of the literature is to combine computer vision technology and use theoretical results in the field of pattern recognition to detect and track pedestrians in video sequences [ 7 ]. Literature is the basis of computer vision's more advanced behavior understanding analysis [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Wang

2022

Computational Intelligence and Neuroscience

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Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical results in the field of pattern recognition and computer vision technology, we are committed to detect and track pedestrians from video sequences. In addition to computer vision-based passer-by detection and tracking technology as the key, in the advanced computer vision action and analysis, it has a direct impact on the accuracy and robustness of its understanding. We analyzed various targets, such as subsequent recognition motion and pedestrian motion, and described them as high-level application processing, such as action understanding. In addition, because of the unique texture of human clothes compared with the surrounding natural landscape, they are highly “prominent” from the perspective of human visual system, and they are particularly prominent in the peripheral part of human contact with the background. In this paper, a binary function based on importance is proposed. As the space representation of image itself is not sensitive to noise and local signal, space representation is used. In addition, as an observation model, it can reduce the adverse effects of background noise and local noise on the tracking algorithm. Through the function block tracking, the pedestrian’s body can be tracked in detail. At the same time, the color band learning method is used to update the target template online to deal with the changes of target appearance caused by sunshine, pedestrian posture, and other factors. According to the experimental results, even if the appearance and posture of pedestrians change greatly, it has a stable tracking effect.

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Section: Related Workmentioning

confidence: 99%

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Wang

2022

Computational Intelligence and Neuroscience

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“…To the best of our knowledge, only two Re-ID works [ 17 , 54 ] have been proposed with IR–IR matching. In [ 17 ], the authors introduced a person re-identification approach utilizing local features in infrared image sequences.…”

Section: Single-modal Person Re-idmentioning

confidence: 99%

“…In [ 54 ], Coşar et al proposed a re-identification method for service robots using thermal images, in which the authors performed entropy-based sampling during training to build thermal dictionaries of humans and then converted the sequence of dictionary elements into a symbolic form. This new representation of symbols was used as a feature to train SVM classifiers.…”

Section: Single-modal Person Re-idmentioning

confidence: 99%

“…In the Re-ID literature, publicly available RGB–D and IR sensor-based datasets [ 4 , 14 , 33 , 35 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] have been recorded for specific purposes because some Re-ID approaches use different domains combinedly to improve the performance of Re-ID [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 47 , 48 ] while other approaches use cross-domain methods [ 49 , 50 , 51 , 52 , 53 ] in the Re-ID framework to continue surveillance between daytime visible images and nighttime infrared images. Still, some Re-ID methods consider only IR–IR [ 17 , 54 ] matching using the publicly available datasets.…”

Section: Introductionmentioning

confidence: 99%

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Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Uddin

Bhuiyan

Bappee

et al. 2023

Sensors

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Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.

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“…A re-identification method for a robot was presented in [ 24 ] using thermal image entropy-based sampling to obtain a thermal dictionary for training a support vector machine (SVM) classifier after the head region was segmented for each person. In [ 25 ], a person tracking and identification method for a mobile robot was presented by combining three features from two laser range finders (LRFs) and a camera.…”

Section: Related Workmentioning

confidence: 99%

Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots

Algabri

Choi

2022

Sensors

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It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.

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Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling

Cited by 14 publications

References 31 publications

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots

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