scholarly journals Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1116
Author(s):  
Jie Bai ◽  
Sen Li ◽  
Han Zhang ◽  
Libo Huang ◽  
Ping Wang
Keyword(s):  
Target Detection ◽  
Traffic Safety ◽  
Gaussian Mixture ◽  
Transportation Systems ◽  
Fusion Algorithm ◽  
Smart Cameras ◽  
Probability Hypothesis Density ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Perception System

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

Target detection and tracking with a high frequency ground wave radar

1994 ◽  
Vol 19 (4) ◽  
pp. 540-548 ◽  
Author(s):  
R. Khan ◽  
B. Gamberg ◽  
D. Power ◽  
J. Walsh ◽  
B. Dawe ◽  
...  
Keyword(s):  
Target Detection ◽  
High Frequency ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Ground Wave

Radar as a Key to Global Aeroecology

2022 ◽  
pp. 482-505
Author(s):  
Alexey Noskov
Keyword(s):  
Big Data ◽  
Pest Management ◽  
Target Detection ◽  
Technical Progress ◽  
Continuous Wave ◽  
Insect Species ◽  
Detection And Tracking ◽  
Insect Biodiversity ◽  
Wave Radar ◽  
History Of

Open, systematic, and global approaches are needed to address the challenges of aeroconservation and pest management. Recent technical progress enables deeper investigation and understanding of aeroecology. Radar plays a central role in flying species monitoring in the global scope. The technology provides various ways of target detection and tracking, working for multiple ranges and different visibility. The existing technology allows deploying global monitoring of avian and insect species. This work discusses the essentials of the technology and the history of its application for bird and insect detection. The author describes the development of the topic according to the main groups of radar approaches: pulsed sets, vertical-looking solutions, harmonic systems, and efficient frequency modulated continuous wave radar. Advances in big data processing, robotics, computation, and communications enable practitioners to combine the discussed radar solutions aiming at global avian and insect biodiversity monitoring and negative human impact systematic estimation.

scholarly journals An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Guoliang Zhang ◽  
Chunling Yang ◽  
Yan Zhang
Keyword(s):  
Particle Filtering ◽  
Synthetic Data ◽  
Probability Of Detection ◽  
Multispectral Image ◽  
Fusion Algorithm ◽  
Multiple Targets ◽  
Probability Hypothesis Density ◽  
Multispectral Data ◽  
Detection Model ◽  
Detection And Tracking

In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets. This algorithm can deal with the nonlinear measurement on target state. In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter. Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach. The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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