scholarly journals Organ Measurement Domain

2020 ◽  
Author(s):  
Keyword(s):  
Measurement Domain

scholarly journals Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3702 ◽  
Author(s):  
Chiman Kwan ◽  
Bryan Chou ◽  
Jonathan Yang ◽  
Akshay Rangamani ◽  
Trac Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Compressive Sensing ◽  
Target Tracking ◽  
Low Power Consumption ◽  
Coded Aperture ◽  
Learning Approach ◽  
Residual Network ◽  
Practical Applications ◽  
Measurement Domain

Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.

scholarly journals Extraction of Weak Scatterer Features Based on Multipath Exploitation in Radar Imagery

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Muhannad Almutiry ◽  
Lorenzo Lo Monte ◽  
Michael C. Wicks
Keyword(s):  
Inverse Problem ◽  
Model Simulation ◽  
Forward Model ◽  
Secondary Source ◽  
Scene Modeling ◽  
Scattering Effects ◽  
Measurement Domain ◽  
Radar Imagery ◽  
Nonlinear Matrix ◽  
Multipath Scattering

We proposed an improved solution to two problems. The first problem is caused by the sidelobe of the dominant scatterer masking a weak scatterer. The proposed solution is to suppress the dominant scatterer by modeling its electromagnetic effects as a secondary source or “extra dependent transmitter” in the measurement domain. The suppression of the domain scatterer reveals the presence of the weak scatterer based on exploitation of multipath effects. The second problem is linearizing the mathematical forward model in the measurement domain. Improving the quantity of the prediction, including multipath scattering effects (neglected under the Born approximation), allows us to solve the inverse problem. The multiple bounce (multipath) scattering effect is the interaction of more than one target in the scene. Modeling reflections from one target towards another as a transmitting dipole will add the multiple scattering effects to the scattering field and permit us to solve a linear inverse problem without sophisticated solutions of a nonlinear matrix in the forward model. Simulation results are presented to validate the concept.

scholarly journals Vehicle-Type Detection Based on Compressed Sensing and Deep Learning in Vehicular Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4500 ◽  
Author(s):  
Yinghua Li ◽  
Bin Song ◽  
Xu Kang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Compressed Sensing ◽  
Vehicular Networks ◽  
Traffic Monitoring ◽  
Saliency Map ◽  
Monitoring And Control ◽  
Practical Applications ◽  
Vehicle Type ◽  
Traffic Regulation ◽  
Reference Information ◽  
Measurement Domain

Throughout the past decade, vehicular networks have attracted a great deal of interest in various fields. The increasing number of vehicles has led to challenges in traffic regulation. Vehicle-type detection is an important research topic that has found various applications in numerous fields. Its main purpose is to extract the different features of vehicles from videos or pictures captured by traffic surveillance so as to identify the types of vehicles, and then provide reference information for traffic monitoring and control. In this paper, we propose a step-forward vehicle-detection and -classification method using a saliency map and the convolutional neural-network (CNN) technique. Specifically, compressed-sensing (CS) theory is applied to generate the saliency map to label the vehicles in an image, and the CNN scheme is then used to classify them. We applied the concept of the saliency map to search the image for target vehicles: this step is based on the use of the saliency map to minimize redundant areas. CS was used to measure the image of interest and obtain its saliency in the measurement domain. Because the data in the measurement domain are much smaller than those in the pixel domain, saliency maps can be generated at a low computation cost and faster speed. Then, based on the saliency map, we identified the target vehicles and classified them into different types using the CNN. The experimental results show that our method is able to speed up the window-calibrating stages of CNN-based image classification. Moreover, our proposed method has better overall performance in vehicle-type detection compared with other methods. It has very broad prospects for practical applications in vehicular networks.

scholarly journals Detection and Confirmation of Multiple Human Targets Using Pixel-Wise Code Aperture Measurements

2020 ◽  
Vol 6 (6) ◽  
pp. 40 ◽  
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Akshay Rangamani ◽  
Trac Tran ◽  
Jack Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Storage ◽  
Target Detection ◽  
Vehicle Detection ◽  
Residual Network ◽  
Reconstruction Process ◽  
Recent Approach ◽  
Measurement Domain ◽  
Multiple Frames

Compressive video measurements can save bandwidth and data storage. However, conventional approaches to target detection require the compressive measurements to be reconstructed before any detectors are applied. This is not only time consuming but also may lose information in the reconstruction process. In this paper, we summarized the application of a recent approach to vehicle detection and classification directly in the compressive measurement domain to human targets. The raw videos were collected using a pixel-wise code exposure (PCE) camera, which condensed multiple frames into one frame. A combination of two deep learning-based algorithms (you only look once (YOLO) and residual network (ResNet)) was used for detection and confirmation. Optical and mid-wave infrared (MWIR) videos from a well-known database (SENSIAC) were used in our experiments. Extensive experiments demonstrated that the proposed framework was feasible for target detection up to 1500 m, but target confirmation needs more research.

scholarly journals Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5347
Author(s):  
Chaoxin He ◽  
Min Zhang ◽  
Guizhou Wu ◽  
Fucheng Guo
Keyword(s):  
Linear Time ◽  
Data Association ◽  
Region Of Interest ◽  
Sensor Data ◽  
Time Consumption ◽  
Assignment Algorithm ◽  
Measurement Domain ◽  
Multi Target Tracking ◽  
Direct Data ◽  
Passive Sensor

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.

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