Target Tracking Algorithm Research Articles

Moving Target Tracking Method Based on Improved Camshift

<p>Aiming at the problem that target occlusion and other disturbances in complex background will reduce the tracking accuracy of moving target, and even lead to tracking failure, this paper proposes a moving target tracking algorithm based on the improved Camshift algorithm. Firstly, Gaussian background is used to model the foreground image to improve the backprojection image, and then the interference of backprojection is removed to improve the tracking effect in complex background conditions. Secondly, Kalman filtering is utilized to predict the trajectory, which further improves the tracking accuracy of Camshift algorithm in occlusion condition. A lot of experiments are processed, and the results show that the proposed algorithm could effectively improve the tracking accuracy and meet the real-time requirements.</p> <p> </p>

Enhanced Target Tracking Algorithm for Autonomous Driving Based on Visible and Infrared Image Fusion

Enhanced Target Tracking Algorithm for Autonomous Driving Based on Visible and Infrared Image Fusion

Exploration of agricultural IoT breeding tracking based on a saliency visual targettracking algorithm

Exploration of agricultural IoT breeding tracking based on a saliency visual targettracking algorithm

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

A Computer Vision-Based Algorithm for Detecting Vehicle Yielding to Pedestrians

Computer vision has made remarkable progress in traffic surveillance, but determining whether a motor vehicle yields to pedestrians still requires considerable human effort. This study proposes an automated method for detecting whether a vehicle yields to pedestrians in intelligent transportation systems. The method employs a target-tracking algorithm that uses feature maps and license plate IDs to track the motion of relevant elements in the camera’s field of view. By analyzing the positions of motor vehicles and pedestrians over time, we predict the warning points of pedestrians and hazardous areas in front of vehicles to determine whether the vehicles yield to pedestrians. Extensive experiments are conducted on the MOT16 dataset, real traffic street scene video dataset, and a Unity3D virtual simulation scene dataset combined with SUMO, which demonstrating the superiority of this tracking algorithms. Compared to the current state-of-the-art methods, this method demonstrates significant improvements in processing speed without compromising accuracy. Specifically, this approach substantially outperforms in operational efficiency, thus catering aptly to real-time recognition requirements. This meticulous experimentation and evaluations reveal a commendable reduction in ID switches, enhancing the reliability of violation attributions to the correct vehicles. Such enhancement is crucial in practical urban settings characterized by dynamic interactions and variable conditions. This approach can be applied in various weather, time, and road conditions, achieving high predictive accuracy and interpretability in detecting vehicle–pedestrian interactions. This advanced algorithm illuminates the viable pathways for integrating technological innovation and sustainability, paving the way for more resilient and intelligent urban ecosystems.

Research on image recognition and processing of motion targets of warehouse logistics robots

Abstract In developing robots for warehouse logistics, image recognition and processing for moving targets are the cornerstone of subsequent work. In this paper, the Meanshift algorithm is extended to continuous image sequences, and the Camshift algorithm for motion target tracking in a warehouse environment is proposed to obtain effective tracking of targets through the probability distribution when the color of continuous images changes dynamically. Based on target tracking, a feature-matching-based image recognition method is constructed. The scene image is first treated with improved Gamma correction for light equalization, and then image features are extracted using SURF feature points. Regarding running time, the feature matching method is, on average, 2.03 seconds faster than FLDA and 0.96 seconds faster than PCAFLDA under the same external conditions. By optimizing the computational structure, the feature-matching method can address the need for efficiency in warehouse logistics.

Improved YOLOv7 Intelligent Environment Perception Algorithm Based on Loss Function

To address the difficulty of environment perception in unmanned driving technology, an improved intelligent environment perception model based on YOLOv7 loss function is proposed. Simultaneously, the algorithm of deep sort target tracking is added for the sake of perceiving the surrounding environment through the camera to ensure the safety of the driver. And by modifying the loss term of the aspect ratio to the distinction between the predicted width and the target width, the fitting speed of the model can be effectively improved. Focal Loss is introduced to optimize the problem with the sample imbalance inside the target bounding box regression and reduce the weight proportion of numerous anchor boxes with less overlap between the predicted target bounding box and the true target bounding one to the overall model, such that the model centers more upon overlapping targets. The data applied to this article is BDD100K. The map of this algorithm reaches 60.3 %, which reaches 2.2 percentage points exceeding the baseline. Tests indicate that the raised algorithm attains certain advancements in the intelligent perception model of unmanned driving.

Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique

Abstract In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.

Generative Target Tracking Method with Improved Generative Adversarial Network

Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, using the improved you only look once multitarget association algorithm to classify and detect the position of the target to be detected in the current frame, constructing a feature extraction model using generative adversarial networks (GANs) to learn the main features and subtle features of the target, and then using GANs to generate the motion trajectories of multiple targets, finally fuzing the motion and appearance information of the target to obtain the optimal match. The optimal matching of the tracked targets is obtained. The experimental results under OTB2015 and IVOT2018 datasets demonstrate that the proposed multitarget tracking algorithm has high accuracy and robustness, with 65% less jumps and 0.25% more accuracy than the current algorithms with minimal identity exchange and jumps.

Dynamic visible light positioning based on enhanced visual target tracking

In visible light positioning systems, some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy, real-time performance, and robustness. However, there are still two problems: (1) When the captured LED disappears and the uncertain LED reappears, existing tracking algorithms may recognize the landmark in error; (2) The receiver is not always able to achieve positioning under various moving statuses. In this paper, we propose an enhanced visual target tracking algorithm to solve the above problems. First, we design the lightweight recognition/demodulation mechanism, which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark. Then, we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning, when the receiver is under various moving statuses. Experimental results show that our system can achieve high-precision dynamic positioning and improve the system's comprehensive performance.

Coordinated target localization and tracking with node scheduling for a network of autonomous underwater vehicles

Underwater mobile sensor networks (UMSNs) have the advantages of large area coverage, flexible network structure, and good local resolution. Particularly, spatial diversity can be exploited through node mobility to get more target information. To take those advantages in the context of regional cooperative target localization and tracking, a joint node scheduling and acoustic model fusion method is proposed for an UMSN consisting of multiple autonomous underwater vehicles (AUVs). First, a target tracking algorithm framework based on the random finite set theory is established. To take the acoustic environment into account, the a priori acoustic field and target distribution information are combined to evaluate the detection probability. A path-planning scheme with node scheduling is then developed based on the information gain to optimize the detection performance. To achieve effective node scheduling, three reward functions with different information gains respectively derived from the Rényi divergence and Fisher information matrix are proposed and compared. The rationale and the performance of the developed method are verified via simulations. A simplified version of the approach is also tested in field experiments with three AUVs, verifying the effectiveness of node scheduling in a realistic scenario.

Detection of Pig Movement and Aggression Using Deep Learning Approaches.

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m2 space; they were bred in stable social groups and a video was set up to record the whole day's activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig's identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R2 of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.

A variable structure multi‐model maneuvering target tracking algorithm based on Monte Carlo learning

AbstractA well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy.

A deformation measurement method based on surface texture information of rocks and its application

Users of the digital image correlation method are faced with the problem of poor operability, low repeatability, and lack of standardized specifications for spraying speckles. To solve the problem, the research proposed a rock deformation measurement method that obviates the need to spray speckles. A local binary model was established by using the local binary pattern (LBP) operator based on deep texture features on rock surfaces. The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability. Based on the LBP digital speckle pattern, the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks. The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone. Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks. The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles: this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

Transformer-based target tracking algorithm for space-based optoelectronic detection

The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.

B-spline based Extended target tracking technology in cluttered environments

In order to solve the problem of tracking single extended targets in a cluttered environment, this thesis proposes a B-spline based probabilistic data association extended target tracking method (ET-BS-PDA). Firstly, the state of the extended target is modelled using B-sample strips, secondly, all events associated with the extended target will be counted based on valid measurements, and the probability of the associated events will be computed using the full probability principle. Finally, we use probabilistic data association algorithms to update state and covariance of extended targets and verify effectiveness of extended target tracking algorithms in cluttered environments through simulations.

Microscopic mechanical properties and fabric anisotropic evolution law of open graded gravel permeable base under dynamic loading

Under traffic dynamic load, the microscopic mechanical properties and the fabric anisotropic evolution of open graded gravel permeable base directly determine the ability of open graded gravel permeable base to resist external loads and maintain stability in service. Firstly, this article constructed a discrete element numerical model for open graded gravel permeable base, and verified the accuracy of the model through experiments. Then, the effects of gradation, nominal maximum particle size, friction coefficient between aggregate particles, and effective elastic modulus of aggregate on the microscopic mechanical properties, migration behavior, and the fabric anisotropic evolution of open graded gravel aggregate particles were studied in sequence. The research results indicate that the proposed image moving target tracking algorithm based on the three branches twin network can achieve precise tracking and measurement of particle microscopic migration behavior. The method proposed in this study for constructing a three-dimensional surface model of gravel particles based on the principle of multi view averaging and the scale invariant feature transformation algorithm can achieve rapid numerical reconstruction of the three-dimensional morphology of gravel particles at a low cost. The internal aggregate particles of intermediate grade gravel are more likely to form a stable network force chain topology structure that can effectively resist external loads. As the maximum nominal particle size increases, the topological structure of the contact force of gravel particles becomes sparse, and the proportion of strong contact force increases. Increasing the friction coefficient of the contact interface between aggregate particles can to some extent hinder the migration behavior of aggregate particles inside the graded gravel. As the effective elastic modulus of the aggregate increases, the internal network strong contact force chain topology of open graded gravel gradually forms. Under external loads, the migration speed and displacement of the aggregate particles inside open graded gravel decrease, and the degree of internal normal contact force and branch vector anisotropy also decrease.

Tracking of Manoeuvring Target Using Fuzzy Logic Augmented H-Infinity Filter

Conventionally for target tracking, Kalman filter and closely related algorithms are used. These filters inherently are as such not robust. In this paper, fuzzy logic augmented H-infinity filters are proposed for target tracking application. Initially, fuzzy logic is used at filter level to eliminate or reduce local estimation errors and then, an additional fuzzy system is used to minimize effects of outliers and estimation errors during data fusion process. An adaptive H-Infinity filter using fuzzy degree of matching is also proposed for target tracking. The performance of these algorithms is evaluated using MATLAB and its fuzzy logic tool box. Very encouraging results have been obtained with the newly proposed algorithms for target tracking.

Cognitive FDA-MIMO Radar Network for Target Discrimination and Tracking With Main-Lobe Deceptive Trajectory Interference

This paper propose a cognitive FDA-MIMO radar network (CFDA-MIMON) target discrimination and tracking algorithm under main-lobe deceptive trajectory interference. This algorithm can be divided into two stages: search-discrimination-initialization and cognitive target tracking. The first stage mainly uses the distribution characteristics of target and main-lobe deceptive trajectory interference on different radars to discriminate the target and interference in the radar network. A target state initialization strategy driven by G-pair measurements with large time interval is proposed for target tracking. In the second stage, a cognitive target tracking algorithm of single FDA-MIMO radar system is designed based on the criteria of maximum Capon power spectrum (CMCP). In addition, in order to avoid the divergence problem of the EKF filter, a target state correction method (TSC) based on auxiliary particles is proposed. Finally, the fusion strategy of multiple radar trajectories is used to alleviate the estimation error of the single radar. Numerical experiments verify that the proposed CFDA-MIMON target discrimination and tracking algorithm can distinguish the target from main-lobe deceptive trajectory interference without prior information of the target, and output the target's trajectory.

Improved TransT Target Tracking Algorithm Based on Hybrid Attention

The TransT method pioneered the introduction of the attention mechanism into the target tracking field, but there are still shortcomings in stability. To improve the stability of the algorithm, we propose an improved feature fusion algorithm for visual image target tracking, and we adopt the LKA attention mechanism to strengthen the local attention to the target to ensure the tracker achieves both global and local attention. The tracking of visual image targets is achieved. The experimental results show that the proposed method has a short execution time and high tracking accuracy.