scholarly journals Moving Vehicle Tracking Optimization Method Based on SPF

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Caixia Lv ◽  
Xuejing Zhang
Keyword(s):  
Behavior Analysis ◽  
Adjacency Matrix ◽  
Intelligent Transportation System ◽  
Red Light ◽  
Optimization Method ◽  
Vehicle Tracking ◽  
Experimental Simulation ◽  
Target Area ◽  
Tracking Accuracy ◽  
Moving Vehicle

In the intelligent transportation system, the license information can be automatically recognized by the computer and the vehicle can be tracked. Red light running, illegal change of lanes, vehicle retrograde, and other illegal driving events are reasonably recorded. This is undoubtedly an effective help for the traffic police to relieve the huge work pressure. However, in China, a considerable number of vehicle tracking methods have certain limitations in resisting complex external environmental influences. The external environmental factors include but not limited to variable factors such as camera movement, jitter, and severe rain and snow. These factors cannot be controlled well, so the tracking accuracy is greatly reduced. In regard to this, this paper proposes an optimization method for moving vehicle tracking based on SPF. First, according to the size of the overlapping area of the motion area between the two images, the researcher can construct and simplify the vertex adjacency matrix that reflects the characteristics of the undirected bipartite graph. Then according to the corresponding relationship between the vertex adjacency matrix and the regional behavior and vehicle behavior, the researcher completes the regional behavior analysis and vehicle behavior analysis. On this basis, a particle filter vehicle tracking algorithm based on segmentation compensation is introduced, and the vector sum of the tracked segmentation area is used as the final position of the target vehicle. In this way, as many scattered particles fall on the target area as possible, which will greatly improve the efficiency of particle utilization, enhance tracking accuracy, and avoid the problem of tracking failure caused by too fast vehicle movement. Through experimental simulation, it can be seen that the method proposed in this paper can greatly enhance the vehicle tracking ability when tracking vehicles in “complex environments.”

scholarly journals Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

2021 ◽  
Vol 13 (14) ◽  
pp. 2770
Author(s):  
Shengjing Tian ◽  
Xiuping Liu ◽  
Meng Liu ◽  
Yuhao Bian ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Vehicle Tracking ◽  
Complex Nature ◽  
Global Feature ◽  
Tracking Accuracy ◽  
State Estimator ◽  
Latent Space ◽  
3D Shapes ◽  
Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

scholarly journals Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3658
Author(s):  
Qingfeng Zhu ◽  
Sai Ji ◽  
Jian Shen ◽  
Yongjun Ren
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Security Analysis ◽  
Geographical Location ◽  
Experimental Simulation ◽  
Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

scholarly journals Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2106 ◽  
Author(s):  
Jiuchao Zhao ◽  
Anxi Yu ◽  
Yongsheng Zhang ◽  
Xiaoxiang Zhu ◽  
Zhen Dong
Keyword(s):  
Surface Deformation ◽  
Design Method ◽  
Geometric Model ◽  
Three Dimensional ◽  
Optimization Method ◽  
Deformation Measurement ◽  
Experimental Simulation ◽  
Baseline Design ◽  
Imaging Performance ◽  
The Impact

Spaceborne multistatic synthetic aperture radar (SAR) tomography (SMS-TomoSAR) systems take full advantage of the flexible configuration of multistatic SAR in the space, time, phase, and frequency dimensions, and simultaneously achieve high-precision height resolution and low-deformation measurement of three-dimensional ground scenes. SMS-TomoSAR currently poses a series of key issues to solve, such as baseline optimization, spatial transmission error estimation and compensation, and the choice of imaging algorithm, which directly affects the performance of height-dimensional imaging and surface deformation measurement. This paper explores the impact of baseline distribution on height-dimensional imaging performance for the baseline optimization issue, and proposes a feasible baseline optimization method. Firstly, the multi-base multi-pass baselines of an SMS-TomoSAR system are considered equivalent to a group of multi-pass baselines from monostatic SAR. Secondly, we establish the equivalent baselines as a symmetric-geometric model to characterize the non-uniform characteristic of baseline distribution. Through experimental simulation and model analysis, an approximately uniform baseline distribution is shown to have better SMS-TomoSAR imaging performance in the height direction. Further, a baseline design method under uniform-perturbation sampling with Gaussian distribution error is proposed. Finally, the imaging performance of different levels of perturbation is compared, and the maximum baseline perturbation allowed by the system is given.

scholarly journals Least Trimmed Squares Approach to Lucas-Kanade Algorithm in Object Tracking Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Yih-Lon Lin
Keyword(s):  
Cost Function ◽  
Object Tracking ◽  
Vehicle Tracking ◽  
Tracking Accuracy ◽  
Least Trimmed Squares ◽  
Environment Adaptation ◽  
Tracking Process ◽  
Important Research Topic ◽  
Process Simulations ◽  
The Cost

The object tracking problem is an important research topic in computer vision. For real applications such as vehicle tracking and face tracking, there are many efficient and real-time algorithms. In this study, we will focus on the Lucas-Kanade (LK) algorithm for object tracking. Although this method is time consuming, it is effective in tracking accuracy and environment adaptation. In the standard LK method, the sum of squared errors is used as the cost function, while least trimmed squares is adopted as the cost function in this study. The resulting estimator is robust against outliers caused by noises and occlusions in the tracking process. Simulations are provided to show that the proposed algorithm outperforms the standard LK method in the sense that it is robust against the outliers in the object tracking problems.

scholarly journals Laser-driven Wireless Deep Brain Stimulation using Temporal Interference and Organic Electrolytic Photocapacitors

2021 ◽  
Author(s):  
Florian MISSEY ◽  
Mary Jocelyn DONAHUE ◽  
Pascal WEBER ◽  
Ibrahima NGOM ◽  
Emma ACERBO ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Ex Vivo ◽  
Neuronal Response ◽  
Red Light ◽  
Animal Experiments ◽  
Target Area ◽  
Implanted Electrodes ◽  
Deep Brain

Deep brain stimulation (DBS) is a technique commonly used both in clinical and fundamental neurosciences. Classically, brain stimulation requires an implanted and wired electrode system to deliver stimulation directly to the target area. Although techniques such as temporal interference (TI) can provide stimulation at depth without involving any implanted electrodes, these methods still rely on a wired apparatus which limits free movement. Herein we report organic photocapacitors as untethered light-driven electrodes which convert deep-red light into electric current. Pairs of these ultrathin devices can be driven using lasers at two different frequencies to deliver stimulation at depth via temporally interfering fields. We validate this concept of laser TI stimulation using numerical modeling, ex vivo tests with phantom samples, and finally in vivo tests. Wireless organic photocapacitors are placed on the cortex and elicit stimulation in the hippocampus, while not delivering off-target stimulation in the cortex. This laser-driven wireless TI evoked a neuronal response at depth that is comparable to control experiments induced with deep brain stimulation protocols using implanted electrodes. Our work shows that a combination of these two techniques, temporal interference and organic electrolytic photocapacitors, provides a reliable way to target brain structures requiring neither deeply implanted electrodes nor tethered stimulator devices. The laser TI protocol demonstrated here address two of the most important drawbacks in the field of deep brain stimulation and thus holds potential to solve many issues in freely-moving animal experiments or for clinical chronic therapy application.

scholarly journals Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei Zhan ◽  
Yafeng Zou ◽  
Zhangzhang He ◽  
Zhiliang Zhang
Keyword(s):  
Behavior Analysis ◽  
Observation Time ◽  
Experimental Results ◽  
Video Frame ◽  
Time Interval ◽  
Tracking Accuracy ◽  
Accuracy Rate ◽  
Grooming Behavior ◽  
Behavior Recognition ◽  
Key Points

Statistical analysis of Bactrocera grooming behavior is important for pest control and human health. Based on DeepLabCut, this study proposes a noninvasive and effective method to track the key points of Bactrocera minax and to detect and analyze its grooming behavior. The results are analyzed and calculated automatically by a computer program. Traditional movement tracking methods are invasive; for instance, the use of artificial pheromone may affect the behavior of Bactrocera minax, thus directly affecting the accuracy and reliability of experimental results. Traditional research studies mainly rely on manual work for behavior analysis and statistics. Researchers need to play the video frame by frame and record the time interval of each grooming behavior manually, which is time-consuming, laborious, and inaccurate. So the advantages of automated analysis are obvious. Using the method proposed in this paper, the image data of 94538 frames from 5 adult Bactrocera were analyzed and 14 key points were tracked. The overall tracking accuracy was as high as 96.7%. In the behavior analysis and statistics, the average accuracy rate of the five grooming behavior was all above 96%, and the accuracy rate of the remaining two grooming behavior was over 87%. The experimental results show that the automatic noninvasive method designed in this paper can track many key points of Bactrocera minax with high accuracy and ensure the accuracy of insect behavior recognition and analysis, which greatly reduces the manual observation time and provides a new method for key points tracking and behavior recognition of related insects.

Gesture Tracking and Locating Algorithm Based on Federated Tracking Filter

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Lei Yu ◽  
Junyi Hou ◽  
Shumin Fei
Keyword(s):  
Mean Shift ◽  
Experimental Simulation ◽  
Human Hand ◽  
Sampling Point ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Gesture Tracking ◽  
Tracking Time ◽  
Tracking Filter ◽  
The Impact

Abstract In this paper, a joint gesture tracking method combining particle filter and mean shift algorithm is proposed to improve the accuracy and robustness of the system. During the slow movement of the human hand, the average movement of the particles is first used to cause most of the particles to drift into the gesture area. In the case where the movement of the human hand is faster or there is occlusion, when the mean shift of the particle is performed, if the region of the gesture is not detected, the particle will return to the state before the drift, and then the next frame is processed. The method can directly calculate the position of the gesture based on the particles used for subsequent testing, and can save the tracking time of the algorithm. Through experimental simulation, compared with the Cam-shift algorithm, when the sampling point of the joint tracking algorithm proposed in this paper is 200, the tracking accuracy is improved to 95.2%. Compared with 90.6% of the Cam-shift algorithm, the tracking time is reduced from 83.7 ms to 25.8 ms. Therefore, the proposed algorithm can greatly improve the tracking accuracy and real-time, and can also effectively reduce the impact of complex environments on the tracking effect.

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