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Visual Sensor Networks for Object Detection and Tracking

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 79884

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Prof. Dr. Byung-Gyu Kim

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Department of IT Engineering, Sookmyung Women's University, Seoul, Korea
Interests: visual sensor network; real-time object segmentation; deep learning for object detection; facial expression recognition
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Special Issue Information

Dear Colleagues,

Information obtained through the human eye is more efficient and diverse for object recognition/tracking than information obtained through any other sensory organ. Recently, these kinds of tasks for visual object detection, recognition, and tracking are being enabled by more flexible vision sensors and its network scheme, such as the 5G standard. In addition, visual intelligence technology and inference systems based on deep/reinforcement learning are currently actively being researched to make vision systems more accurate. This issue will publish original technical papers and review papers on these recent technologies which are focusing on visual recognition, real-time visual object tracking, knowledge extraction, distributed visual sensor networks, and applications.

You are welcome to submit an unpublished original research work related to the theme of “Visual Sensor Networks for Object Detection and Tracking.”

Prof. Dr. Byung-Gyu Kim
Guest Editor

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Keywords

Intelligent object detection algorithms
Fast and complexity reduction algorithms for real-time object detection and tracking
Knowledge extraction and mining from visual sensor data
Visual sensor network architecture for object detection and tracking
Awareness-based visual sensor network design
Intelligent machine learning mechanism for object detection and recognition
Lightweight deep learning for real-time object detection and tracking
Visual data representation and transmission in a 5G network
Real-time visual object tracking in vision sensor network
Intelligent CCTV applications

Related Special Issue

Advanced Visual Sensor Networks for Object Detection and Tracking in Sensors (1 article)

Published Papers (14 papers)

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17 pages, 10617 KiB

Open AccessArticle

Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

by Yooho Lee, Dongsan Jun, Byung-Gyu Kim and Hunjoo Lee

Sensors 2021, 21(10), 3351; https://doi.org/10.3390/s21103351 - 12 May 2021

Cited by 9 | Viewed by 2805

Abstract

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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18 pages, 11680 KiB

Open AccessArticle

Face Recognition at a Distance for a Stand-Alone Access Control System

by Hansung Lee, So-Hee Park, Jang-Hee Yoo, Se-Hoon Jung and Jun-Ho Huh

Sensors 2020, 20(3), 785; https://doi.org/10.3390/s20030785 - 31 Jan 2020

Cited by 39 | Viewed by 8524

Abstract

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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19 pages, 1892 KiB

Open AccessArticle

Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

by Ruben Panero Martinez, Ionut Schiopu, Bruno Cornelis and Adrian Munteanu

Sensors 2021, 21(1), 275; https://doi.org/10.3390/s21010275 - 03 Jan 2021

Cited by 12 | Viewed by 3563

Abstract

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with

31.57

average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to

66.25

frames per second on the Jetson AGX Xavier module. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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14 pages, 18531 KiB

Open AccessArticle

Virtual to Real Adaptation of Pedestrian Detectors

by Luca Ciampi, Nicola Messina, Fabrizio Falchi, Claudio Gennaro and Giuseppe Amato

Sensors 2020, 20(18), 5250; https://doi.org/10.3390/s20185250 - 14 Sep 2020

Cited by 27 | Viewed by 3257

Abstract

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks’ critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V (Grand Theft Auto V), where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real domain shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different domain adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our domain adaptation techniques, we can reduce the Synthetic2Real domain shift, making the two domains closer and obtaining a performance improvement when testing the network over the real-world images. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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30 pages, 10992 KiB

Open AccessReview

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

by Danijela Ristić-Durrant, Marten Franke and Kai Michels

Sensors 2021, 21(10), 3452; https://doi.org/10.3390/s21103452 - 15 May 2021

Cited by 39 | Viewed by 10548

Abstract

This paper provides a review of the literature on vision-based on-board obstacle detection and distance estimation in railways. Environment perception is crucial for autonomous detection of obstacles in a vehicle’s surroundings. The use of on-board sensors for road vehicles for this purpose is well established, and advances in Artificial Intelligence and sensing technologies have motivated significant research and development in obstacle detection in the automotive field. However, research and development on obstacle detection in railways has been less extensive. To the best of our knowledge, this is the first comprehensive review of on-board obstacle detection methods for railway applications. This paper reviews currently used sensors, with particular focus on vision sensors due to their dominant use in the field. It then discusses and categorizes the methods based on vision sensors into methods based on traditional Computer Vision and methods based on Artificial Intelligence. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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22 pages, 4797 KiB

Open AccessArticle

A Visual Tracker Offering More Solutions

by Long Zhao, Mubarak Adam Ishag Mahmoud, Honge Ren and Meng Zhu

Sensors 2020, 20(18), 5374; https://doi.org/10.3390/s20185374 - 19 Sep 2020

Cited by 3 | Viewed by 2296

Abstract

Most trackers focus solely on robustness and accuracy. Visual tracking, however, is a long-term problem with a high time limitation. A tracker that is robust, accurate, with long-term sustainability and real-time processing, is of high research value and practical significance. In this paper, we comprehensively consider these requirements in order to propose a new, state-of-the-art tracker with an excellent performance. EfficientNet-B0 is adopted for the first time via neural architecture search technology as the backbone network for the tracking task. This improves the network feature extraction ability and significantly reduces the number of parameters required for the tracker backbone network. In addition, maximal Distance Intersection-over-Union is set as the target estimation method, enhancing network stability and increasing the offline training convergence rate. Channel and spatial dual attention mechanisms are employed in the target classification module to improve the discrimination of the trackers. Furthermore, the conjugate gradient optimization strategy increases the speed of the online learning target classification module. A two-stage search method combined with a screening module is proposed to enable the tracker to cope with sudden target movement and reappearance following a brief disappearance. Our proposed method has an obvious speed advantage compared with pure global searching and achieves an optimal performance on OTB2015, VOT2016, VOT2018-LT, UAV-123 and LaSOT while running at over 50 FPS. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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14 pages, 3167 KiB

Open AccessArticle

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

by Lu Leng, Ziyuan Yang, Cheonshik Kim and Yue Zhang

Sensors 2020, 20(9), 2644; https://doi.org/10.3390/s20092644 - 06 May 2020

Cited by 27 | Viewed by 4151

Abstract

Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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17 pages, 5983 KiB

Open AccessArticle

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

by Fenglong Ding, Zilong Zhuang, Ying Liu, Dong Jiang, Xiaoan Yan and Zhengguang Wang

Sensors 2020, 20(18), 5315; https://doi.org/10.3390/s20185315 - 17 Sep 2020

Cited by 58 | Viewed by 5111

Abstract

Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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46 pages, 7732 KiB

Open AccessArticle

Design of an Interactive Mind Calligraphy System by Affective Computing and Visualization Techniques for Real-Time Reflections of the Writer’s Emotions

by Chao-Ming Wang and Yu-Chen Chen

Sensors 2020, 20(20), 5741; https://doi.org/10.3390/s20205741 - 09 Oct 2020

Cited by 8 | Viewed by 3653

Abstract

A novel interactive system for calligraphy called mind calligraphy that reflects the writer’s emotions in real time by affective computing and visualization techniques is proposed. Differently from traditional calligraphy, which emphasizes artistic expression, the system is designed to visualize the writer’s mental-state changes during writing using audio-visual tools. The writer’s mental state is measured with a brain wave machine to yield attention and meditation signals, which are classified next into the four types of emotion, namely, focusing, relaxation, calmness, and anxiety. These emotion types then are represented both by animations and color palettes for by-standing observers to appreciate. Based on conclusions drawn from data collected from on-site observations, surveys via Likert-scale questionnaires, and semi-structured interviews, the proposed system was improved gradually. The participating writers’ cognitive, emotional, and behavioral engagements in the system were recorded and analyzed to obtain the following findings: (1) the interactions with the system raise the writer’s interest in calligraphy; (2) the proposed system reveals the writer’s emotions during the writing process in real time via animations of mixtures of fish swimming and sounds of raindrops, insects, and thunder; (3) the dynamic visualization of the writer’s emotion through animations and color-palette displays makes the writer understand better the connection of calligraphy and personal emotions; (4) the real-time audio-visual feedback increases the writer’s willingness to continue in calligraphy; and (5) the engagement of the writer in the system with interactions of diversified forms provides the writer with a new experience of calligraphy. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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12 pages, 2144 KiB

Open AccessArticle

Laser Ranging-Assisted Binocular Visual Sensor Tracking System

by Qilong Wang, Yu Zhang, Weichao Shi and Meng Nie

Sensors 2020, 20(3), 688; https://doi.org/10.3390/s20030688 - 27 Jan 2020

Cited by 11 | Viewed by 3086

Abstract

Aimed at improving the low measurement accuracy of the binocular vision sensor along the optical axis in the process of target tracking, we proposed a method for auxiliary correction using a laser-ranging sensor in this paper. In the process of system measurement, limited to the mechanical performance of the two-dimensional turntable, the measurement value of a laser-ranging sensor is lagged. In this paper, the lag information is updated directly to solve the time delay. Moreover, in order to give full play to the advantages of binocular vision sensors and laser-ranging sensors in target tracking, federated filtering is used to improve the information utilization and measurement accuracy and to solve the estimated correlation. The experimental results show that the real-time and measurement accuracy of the laser ranging-assisted binocular visual-tracking system is improved by the direct update algorithm and the federal filtering algorithm. The results of this paper are significant for binocular vision sensors and laser-ranging sensors in engineering applications involving target tracking systems. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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25 pages, 10281 KiB

Open AccessArticle

A Runway Safety System Based on Vertically Oriented Stereovision

by Dawid Gradolewski, Damian Dziak, Damian Kaniecki, Adam Jaworski, Michal Skakuj and Wlodek J. Kulesza

Sensors 2021, 21(4), 1464; https://doi.org/10.3390/s21041464 - 20 Feb 2021

Cited by 4 | Viewed by 3304

Abstract

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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16 pages, 4790 KiB

Open AccessArticle

Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation

by Yiqing Zhang, Jun Chu, Lu Leng and Jun Miao

Sensors 2020, 20(4), 1010; https://doi.org/10.3390/s20041010 - 13 Feb 2020

Cited by 136 | Viewed by 13510

Abstract

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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18 pages, 3127 KiB

Open AccessArticle

Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method

by Haipeng Zhao, Yang Zhou, Long Zhang, Yangzhao Peng, Xiaofei Hu, Haojie Peng and Xinyue Cai

Sensors 2020, 20(7), 1861; https://doi.org/10.3390/s20071861 - 27 Mar 2020

Cited by 79 | Viewed by 11213

Abstract

Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices. Based on YOLO-LITE as the backbone network, Mixed YOLOv3-LITE supplements residual block (ResBlocks) and parallel high-to-low resolution subnetworks, fully utilizes shallow network characteristics while increasing network depth, and uses a “shallow and narrow” convolution layer to build a detector, thereby achieving an optimal balance between detection precision and speed when used with non-GPU based computers and portable terminal devices. The experimental results obtained in this study reveal that the size of the proposed Mixed YOLOv3-LITE network model is 20.5 MB, which is 91.70%, 38.07%, and 74.25% smaller than YOLOv3, tiny-YOLOv3, and SlimYOLOv3-spp3-50, respectively. The mean average precision (mAP) achieved using the PASCAL VOC 2007 dataset is 48.25%, which is 14.48% higher than that of YOLO-LITE. When the VisDrone 2018-Det dataset is used, the mAP achieved with the Mixed YOLOv3-LITE network model is 28.50%, which is 18.50% and 2.70% higher than tiny-YOLOv3 and SlimYOLOv3-spp3-50, respectively. The results prove that Mixed YOLOv3-LITE can achieve higher efficiency and better performance on mobile terminals and other devices. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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19 pages, 4915 KiB

Open AccessArticle

Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

by Chuljoong Kim and Hanseok Ko

Sensors 2020, 20(15), 4081; https://doi.org/10.3390/s20154081 - 22 Jul 2020

Cited by 3 | Viewed by 2955

Abstract

Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second. Full article

(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)

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