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A Kalman-filter-based fusion method for accurate urban localisation

IET COMMUNICATIONS 2021年第5期15卷 653-663页

作者： Alfakih, Marwan Keche, Mokhtar Benoudnine, Hadjira Univ Sci & Technol Oran Mohamed Boudiaf Dept Elect Signals & Images Lab BP 1505 El Mnouer 31000 Oran Algeria Univ Abdelhamid Ibn Badis Fac Sci & Technol Elect & Elect Dept Mostaganem Algeria

The outage and degradation of the global navigation satellite system (GNSS) signals caused by the multipath phenomena reduce the location accuracy of these systems in urban environment. Hence, integrating an additional localisation technology with the GNSS, so that each technology complements the weakness of the other one, is an efficient solution to improve this accuracy. The widespread availability of the Wi-Fi technology makes it the most appropriate additional technology. In this work, a fusion algorithm based on a Kalman filter is used to integrate the GPS localisation with Wi-Fi fingerprinting localisation in urban environment. The fusion algorithm uses the positions delivered by these two systems to achieve an accurate estimation of the mobile position. The experimental results show that the performance of the proposed fusion method is more accurate than those of the individual methods and other fusion methods from the literature.

关键词： computer vision and image processing techniques Kalman-filter-based fusion method location accuracy Radionavigation and direction finding individual methods urban environment Wi-Fi technology GPS localisation accurate urban localisation image fusion Mobile radio systems Optical, image and video signal processing widespread availability computer communications global navigation satellite system signals satellite navigation fusion algorithm indoor radio wireless LAN Kalman filters additional localisation technology GNSS degradation Global Positioning System appropriate additional technology sensor fusion outage Wi-Fi fingerprinting localisation Kalman filter multipath phenomena

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An improved symbol reduction technique based Huffman coder for efficient entropy coding in the transform coders

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IET image processing 2021年第4期15卷 1008-1022页

作者： Thakur, Vikrant Singh Thakur, Kavita Gupta, Shubhrata Natl Inst Technol Dept Elect Engn Raipur 492001 Madhya Pradesh India Pt Ravishankar Shukla Univ SOS Elect & Photon Raipur Madhya Pradesh India

Entropy coding is the essential block of transform coders that losslessly converts the quantized transform coefficients into the bit-stream suitable for transmission or storage. Usually, the entropy coders exhibit less compression capability than the lossy coding techniques. Hence, in the past decade, several efforts have been made to improve the compression capability of the entropy coding technique. Recently, a symbol reduction technique (SRT) based Huffman coder is developed to achieve higher compression than the existing entropy coders at similar complexity of the regular Huffman coder. However, the SRT-based Huffman coding is not popular for the real-time applications due to the improper negative symbol handling and the additional indexing issues, which restrict its compression gain at most 10-20% over the regular Huffman coder. Hence, in this paper, an improved SRT (ISRT) based Huffman coder is proposed to properly alleviate the deficiencies of the recent SRT-based Huffman coder and to achieve higher compression gains. The proposed entropy coder is extensively evaluated on the ground of compression gain and the time complexity. The results show that the proposed ISRT-based Huffman coder provides significant compression gain against the existing entropy coders with lower time consumptions.

关键词： Codes image and video coding computer vision and image processing techniques

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Classification of human activity detection based on an intelligent regression model in video sequences

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IET image processing 2021年第1期15卷 65-76页

作者： Kumaran, Natarajan Reddy, Uyyala Srinivasulu Natl Inst Technol Dept Comp Applicat Trichy 620015 Tamil Nadu India Natl Inst Technol Dept Comp Applicat Trichy Tamil Nadu India

The most critical objective in security surveillance is abnormal event detection in public scenarios. A scheme is presented for detecting abnormal behaviours in the activities of human groups based on social behaviour analysis. This approach efficiently models group activities than some of the previous strategies that use independent local features. This paper presents a feature descriptor method to signify the movement by implementing the optical flow through covariance matrix coding. The multi-RoI (region of interest) covariance matrix has some frames or patches which could represent the movement in high accuracy. Normal samples are plentiful in public surveillance videos, while there are only a few abnormal samples. For that, the model of a hybridised optical flow covariance matrix is represented in this paper. Optical flow (OF) in the temporal domain is measured as a critical feature of video streams. The logistic regression method is used to detect abnormal activities in a crowded scene. Finally, the behaviours of human crowds can be predicted using benchmark datasets such as UMN, UCSD as well as BEHAVE. The obtained experimental results show that the proposed approach can effectively detect abnormal events from the abandoned environment of surveillance videos.

关键词： Algebra image recognition computer vision and image processing techniques Video signal processing Algebra Regression analysis Regression analysis

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Detecting phone-related pedestrian distracted behaviours via a two-branch convolutional neural network

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第1期15卷 147-158页

作者： Saenz, Humberto Sun, Huiming Wu, Lingtao Zhou, Xuesong Yu, Hongkai Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA Univ Texas Rio Grande Valley Dept Comp Sci Edinburg TX USA Texas A&M Transportat Inst College Stn TX USA Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ USA

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phone-related distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phone-related pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods.

关键词： Optical, image and video signal processing computer vision and image processing techniques Video signal processing Traffic engineering computing

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CNN-based infrared dim small target detection algorithm using target-oriented shallow-deep features and effective small anchor

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IET image processing 2021年第1期15卷 1-15页

作者： Du, Jinming Lu, Huanzhang Hu, Moufa Zhang, Luping Shen, Xinglin Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

For the extremely small size and low signal-to-clutter ratio, target detection in infrared images is still a considerable challenge. Specifically, it is very difficult to detect the point targets because there is no texture and shape information can be used. A target-oriented shallow-deep feature-based detection algorithm is proposed, opening up a promising direction for convolutional neural network-based infrared dim small target detection algorithms. To ensure that small target instances can be used correctly for networks, the effective small anchor is designed according to the shallow layer of ResNet50. To determine whether a detection result belongs to the target, the authors depend on whether the detection centre is included in the ground truth area, rather than on the Intersection Over Union overlap rate, which avoids misjudging the detection result. In this way, small targets can be trained and detected correctly through ResNet50. More importantly, the authors demonstrate that spatially finer shallow features are crucial for small target detection and that semantically stronger deep features are helpful for improving detection probability. Experimental results on simulation data sets and real data sets show that the proposed algorithm can detect the point target when the local signal-to-clutter ratio is approximately 1.3, displaying infinite advantage and great potentiality.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing computer vision and image processing techniques

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Deep Q-network implementation for simulated autonomous vehicle control

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第7期15卷 875-885页

作者： Quek, Yang Thee Koh, Li Ling Koh, Ngiap Tiam Tso, Wai Ann Woo, Wai Lok Republ Polytech Sch Engn 9 Woodlands Ave 9 Singapore 738964 Singapore Newcastle Univ Int Singapore Sch Elect & Elect Engn 172A Ang Mo Kio Ave 8 05-01 Singapore 567739 Singapore Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England

Deep reinforcement learning is poised to be a revolutionised step towards newer possibilities in solving navigation and autonomous vehicle control tasks. Deep Q-network (DQN) is one of the more popular methods of deep reinforcement learning that allows the agent that controls the vehicle to learn through its mistakes based on its actions and interactions with the environment. This paper presents the implementation of DQN to an autonomous self-driving vehicle control in two different simulated environments;first environment is in Python which is a simple 2D environment and then advanced to Unity software separately which is a 3D environment. Based on the scores and pixel inputs, the agent in the vehicle learns and adapts to its surrounding. It develops the best solution strategy to direct itself in the environment where its task is to manoeuvre the vehicle from point to point on a simulated highway scenario. The implemented DQN technique approximates the action value function with convolutional neural network. This evaluates the Q-function for the Q-learning architecture and updates the action value function. This paper shows that DQN is an effective learning method for the agent of an autonomous vehicle. In both simulated environments, the autonomous vehicle gradually learnt the manoeuvre operations and progressively gained the ability to successfully navigate itself and avoid obstacles without prior information of the surrounding.

关键词： Optical, image and video signal processing Neural nets (theory) Learning in AI (theory) Road-traffic system control Mobile robots computer vision and image processing techniques

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Weight-based colour constancy using contrast stretching

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IET image processing 2021年第11期15卷 2424-2440页

作者： Abedini, Zeinab Jamzad, Mansour Sharif Univ Technol Dept Comp Engn Azadi Ave Tehran Iran

One of the main issues in colour image processing is changing objects' colour due to colour of illumination source. Colour constancy methods tend to modify overall image colour as if it was captured under natural light illumination. Without colour constancy, the colour would be an unreliable cue to object identity. Till now, many methods in colour constancy domain are presented. They are in two categories;statistical methods and learning-based methods. This paper presents a new statistical weighted algorithm for illuminant estimation. Weights are adjusted to highlight two key factors in the image for illuminant estimation, that is contrast and brightness. The focus was on the convex part of the contrast stretching function to create the weights. Moreover, a novel partitioning mechanism in the colour domain that leads to improvement in efficiency is proposed. The proposed algorithm is evaluated on two benchmark linear image databases according to two evaluation metrics. The experimental results showed that it is competitive to the statistical state of the art methods. In addition to its low computational cost, it has the advantage of improving the efficiency of statistics-based algorithms for dark images and images with low brightness contrast. Moreover, it is robust to camera change types.

关键词： Optical, image and video signal processing computer vision and image processing techniques Spatial and pictorial databases

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Multi-actuator control with modal switching and different disturbance for scanning imaging motion compensation

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IET CONTROL THEORY AND APPLICATIONS 2021年第15期15卷 1931-1941页

作者： Gao, Yang Xu, Rui Wang, Yutang Tian, Dapeng Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Scanning imaging can be used to obtain wide field-of-view, high-resolution remote-sensing images with a telescope lens. However, the motion between the image and the sensor causes blurring during scanning, which reduces the image quality. In this paper, a motion compensation method based on multi-actuator control with modal switching is proposed. In this method, the tracking controller realizes scanning and compensation;the cooperative controller reduces the relative motion that causes motion blur, and modal switching solves the travel limitation of the linear actuator. Further, to improve the control performance, an adaptive sliding mode controller is proposed and combined with a disturbance observer. The stability theorems for multi-actuator and modal switching are studied. Experimental results show that the accuracy, robustness, and frame rate of the proposed method improved compared with those of traditional methods. Compared with the non-cooperative structure using a PD controller, the root mean square error is reduced by 76.61%.

关键词： Optical, image and video signal processing Actuating and final control devices Interpolation and function approximation (numerical analysis) computer vision and image processing techniques Interpolation and function approximation (numerical analysis) Multivariable control systems Self-adjusting control systems

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Dual branch convolutional neural network for copy move forgery detection

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IET image processing 2021年第3期15卷 656-665页

作者： Goel, Nidhi Kaur, Samarjeet Bala, Ruchika Indira Gandhi Delhi Tech Univ Women Dept Elect & Commun Engn Delhi India Bharati Vidyapeeth Coll Engn Dept Elect & Commun Engn Delhi India Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India

The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning-based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi-scale features by employing different kernel sizes in each branch. Fusion of extracted multi-scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F-2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.

关键词： Optical, image and video signal processing computer vision and image processing techniques Data security

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Evaluation of small unmanned aerial system highway volume and speed-sensing applications

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第1期15卷 84-97页

作者： Ryan, Alyssa Ai, Chengbo Knodler, Michael Fitzpatrick, Cole Univ Massachusetts Dept Civil & Environm Engn 214 Marston Hall130 Nat Resources Rd Amherst MA 01003 USA

Small unmanned aerial systems (sUAS) have been utilised in the transportation industry in recent years to decrease the cost of projects and tasks while increasing safety. This is due to their ability to capture aerial images with reduced effort and time. Recently, these devices have begun to be used for traffic monitoring, given their ability to capture video above a roadway. Combined with object-tracking techniques, vehicle data such as speeds, volumes, and trajectories could be extracted, providing an opportunity to revolutionize traffic data collection techniques. There exists a need to improve upon origin-destination volume and speed data collection procedures through the development of a low-cost methodology to capture detailed data that would allow for more accurate analysis. This study evaluates a methodology and measures the accuracy of volume and speed data collected through sUAS aerial imagery using object tracking techniques. Using the developed methodology, vehicle volumes were tracked at 93% accuracy, and vehicle speeds were recorded with a 6.6% relative error. While future improvements could be made on this methodology as technology advances, this study reveals a low-cost solution to collect vehicle data which could improve the efficiency of transportation studies, and in turn, improve safety.

关键词： Optical, image and video signal processing computer vision and image processing techniques Traffic engineering computing

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