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Sensors for Object Detection, Classification and Tracking II

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 12191

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Special Issue Editors

Dr. Paolo Spagnolo

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Guest Editor

National Research Center of Italy, Lecce, Italy
Interests: object detection and recognition; object re-identification; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Alessia Saggese

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Guest Editor

Department of Electronic and Computer Engineering, University of Salerno, Via Giovanni Paolo II, 132, I83013 Fisciano, SA, Italy
Interests: computer vision; deep learning; video analysis and understanding; robot vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a rapid and successful expansion of computer vision research in several application fields. Object detection is one area that has attained great progress. The intended use of object detection is to determine the location and the class of all (or specific) object instances in an image, and to achieve the temporal tracking of their position.

Algorithms for object detection are strictly dependent on acquisition devices (RGB cameras, thermal, infrared, multi/hyper-spectral). On the other hand, deep neural networks (DNNs) have recently emerged as a powerful machine-learning model able to learn powerful object representations/models without the need to manually design features.

The goal of this Special Issue of Sensors is provide perspective on object detection research. It will be dedicated to highlighting both theoretical and practical aspects of object detection; deep learning-based approaches are welcomed, as are approaches based on unconventional input sensors, such as multispectral or thermal images.

This Special Issue fits within the Scope of Sensors because it explores the topic of object detection from two different points of view. On one hand, new methodologies will be investigated, for example, deep learning-based approaches. On the other hand, emphasis will also be placed on acquisition devices, and approaches able to extract information from multi- and hyperspectral images will also be welcomed.

Dr. Paolo Spagnolo
Dr. Alessia Saggese
Guest Editors

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Keywords

object detection
object tracking
supervised and unsupervised object classification
deep learning algorithms
thermal image analysis
multispectral object analysis
applications

Published Papers (5 papers)

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Research

33 pages, 57076 KiB

Open AccessArticle

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

by Iason-Ioannis Panagos, Angelos P. Giotis, Sokratis Sofianopoulos and Christophoros Nikou

Sensors 2023, 23(23), 9510; https://doi.org/10.3390/s23239510 - 29 Nov 2023

Viewed by 673

Abstract

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction. Full article

(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking II)

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37 pages, 23114 KiB

Open AccessArticle

SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model

by Sumaira Manzoor, Ye-Chan An, Gun-Gyo In, Yueyuan Zhang, Sangmin Kim and Tae-Yong Kuc

Sensors 2023, 23(10), 4906; https://doi.org/10.3390/s23104906 - 19 May 2023

Cited by 2 | Viewed by 1725

Abstract

Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second. Full article

(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking II)

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25 pages, 4682 KiB

Open AccessArticle

Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence

by Parth Goel and Amit Ganatra

Sensors 2023, 23(9), 4436; https://doi.org/10.3390/s23094436 - 30 Apr 2023

Cited by 5 | Viewed by 3911

Abstract

Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. Typically, UDA approaches assume the same categories in both domains. The effectiveness of transfer learning depends on the degree of similarity between the domains, which determines an efficient fine-tuning strategy. Furthermore, domain-specific tasks generally perform well when the feature distributions of the domains are similar. However, utilizing a trained source model directly in the target domain may not generalize effectively due to domain shift. Domain shift can be caused by intra-class variations, camera sensor variations, background variations, and geographical changes. To address these issues, we design an efficient unsupervised domain adaptation network for image classification and object detection that can learn transferable feature representations and reduce the domain shift problem in a unified network. We propose the guided transfer learning approach to select the layers for fine-tuning the model, which enhances feature transferability and utilizes the JS-Divergence to minimize the domain discrepancy between the domains. We evaluate our proposed approaches using multiple benchmark datasets. Our domain adaptive image classification approach achieves 93.2% accuracy on the Office-31 dataset and 75.3% accuracy on the Office-Home dataset. In addition, our domain adaptive object detection approach achieves 51.1% mAP on the Foggy Cityscapes dataset and 72.7% mAP on the Indian Vehicle dataset. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. Experimental results also show that our work significantly outperforms the existing methods. Full article

(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking II)

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24 pages, 1360 KiB

Open AccessArticle

Benchmarking 2D Multi-Object Detection and Tracking Algorithms in Autonomous Vehicle Driving Scenarios

by Diego Gragnaniello, Antonio Greco, Alessia Saggese, Mario Vento and Antonio Vicinanza

Sensors 2023, 23(8), 4024; https://doi.org/10.3390/s23084024 - 16 Apr 2023

Cited by 5 | Viewed by 3000

Abstract

Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and other vehicles on the road. The experimental analyses conducted so far have not thoroughly evaluated the effectiveness of these methods in road driving scenarios. To this aim, we propose in this paper a benchmark of modern multi-object detection and tracking methods applied to image sequences acquired by a camera installed on board the vehicle, namely, on the videos available in the BDD100K dataset. The proposed experimental framework allows to evaluate 22 different combinations of multi-object detection and tracking methods using metrics that highlight the positive contribution and limitations of each module of the considered algorithms. The analysis of the experimental results points out that the best method currently available is the combination of ConvNext and QDTrack, but also that the multi-object tracking methods applied on road images must be substantially improved. Thanks to our analysis, we conclude that the evaluation metrics should be extended by considering specific aspects of the autonomous driving scenarios, such as multi-class problem formulation and distance from the targets, and that the effectiveness of the methods must be evaluated by simulating the impact of the errors on driving safety. Full article

(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking II)

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18 pages, 937 KiB

Open AccessArticle

A Multi-Attention Approach for Person Re-Identification Using Deep Learning

by Shimaa Saber, Souham Meshoul, Khalid Amin, Paweł Pławiak and Mohamed Hammad

Sensors 2023, 23(7), 3678; https://doi.org/10.3390/s23073678 - 2 Apr 2023

Cited by 4 | Viewed by 2126

Abstract

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people’s traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model’s complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications. Full article

(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking II)

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