Improved Otsu Target Tracking Algorithm Based on Iteration

An improved Otsu method using the weighted object variance for defect detection

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

Image-free single-pixel detection, which has the advantages of low computing power requirements, a wide working waveband and high detection speed, provides a new paradigm for target tracking. A dedicated mask-based four-pixel parallel detection system is proposed for ultraviolet (UV) target positioning and tracking. The system consists of four parallel branches. Four masks with linearly increasing transmittance provide one-order geometric moment modulation of the scene simultaneously. The four branches are arranged in a diamond structure to ensure complementarity and symmetry in the x and y directions, respectively. A chrome mask and a photomultiplier tube module are selected to ensure the system performance in the UV waveband. Target positioning is achieved by centroid calculation with a geometric moment and a global system calibration method is proposed to ensure accuracy. The validity of the proposed system is demonstrated with tracking experiments using a UV target. This work provides a practical and low-cost solution for real-time UV target positioning and tracking.

Detection of skin defects on mangoes based on hyperspectral imaging combined with band ratio and improved Otsu method

In this paper an uncooled infrared camera with embedded target detection and tracking capability is presented. The camera is built upon four main electronic cards. Sensor card is designed to read and digitize the analog output signal generated by the infrared (IR) imaging sensor. FPGA card carries out the control process of the data transfer between sensor card and other peripherals. Target detection/tracking task is on the row image data provided by the FPGA card is carried out on the DSP card. Frame thresholding and differencing methods are used for target detection. Kalman filter is utilized for target tracking. According to the experimental results, camera is capable of performing target detection and tracking tasks in real time.

An experimental method of measuring the real contact area of transparent blocks based on the principle of total internal reflection is presented, intending to support the investigation of friction characteristics, heat conduction, and energy dissipation at the contact interface. A laser sheet illuminates the contact interface, and the transmitted laser sheet is projected onto a screen. Then the contact information is acquired from the screen by a camera. An improved Otsu method is proposed to process the data of experimental images. It can compute the threshold of the overall image and filter out all the pixels one by one. Through analyzing the experimental results, we describe the relationship between the real contact area and the positive pressure during a continuous loading process, at different loading rates, with the polymethyl methacrylate (PMMA) material. A hysteresis phenomenon in the relationship between the real contact area and the positive pressure is found and explained.

As a key technology of detection and tracking system, moving target detection and tracking has attracted a great deal of attention. To achieve the goal of tracking target, conventional methods have to complete the detection of targets firstly, and then track the target. However, such kind of approaches require using the method of constant false-alarm rate to detect the whole frame image received at that time, which not only reduces the detection efficiency but also degrades the performance of target tracking. In order to implement real-time monitoring divers and other small moving targets under the conditions of strong underwater noise and clutter interference, an interactive algorithm for moving target detection and tracking is proposed based on acoustic image, which jointly analyzes the target detection system and the target tracking system. In the target tracking unit, the interactive algorithm takes advantage of data interconnection to remove the false targets produced by target detection; extrapolates the target's track, and predicts the location that the target may appear at the next moment by employing the Kalman filter. These messages will be used as priori information in the detection unit. In the detection unit, first of all, a gate is established whose center is the position that the target tracking unit predicted. For the image received at the next moment, detection in the predicted gate in implemented by using the method of constant false-alarm rate, followed by feeding back the detection results to the tracking unit. Compared with existed methods, the proposed algorithm can effectively improve the target detection efficiency and tracking performance, providing a better approach to monitor underwater small targets.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

The on‐board target tracking of unmanned aerial vehicle (UAV) relies on on‐board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real‐time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real‐time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high‐level synthesis technology, and a high‐speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real‐time requirements of UAV target tracking task.

One of the primary techniques for target tracking is the Kalman filter or one of its many variants. While the Kalman filter has been the staple of the tracking community, it has been shown to have drawbacks. When a target performs a maneuver, the tracking solution of the filter can experience deleterious issues. The most common issue is a lag in the position of the target track compared to the true target position as the target performs its maneuver. A more problematic issue occurs when the filter covariance collapses which requires the filter to be reinitialized. While techniques exist that compensate for maneuvers, they rely on detecting the error in the estimated trajectory and the measured target position to generate their response. In this effort, a maneuver detection routine is developed that can be used in conjunction with the standard maneuver compensation approaches. This routine validates the existence of a maneuver more quickly than using inherent detection methods of the other methods. The maneuver detection is performed by an evidence accrual system that uses a fuzzy Kalman filter to incorporate new information and provide a level of evidence that maneuver is occurring. The input data uses behavior characteristics of the Kalman gain vector from the tracking algorithm.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

Video tracking uses the semantic information between video image sequences to process, analyze and study the target to achieve target tracking. In response to the problems of sharp changes in target position, large deformation, and occlusion due to similar background interference, changes in lighting conditions, and different shapes of targets in the target tracking process, the tracking algorithm is less accurate and less robust in target appearance. An improved target tracking algorithm based on multi-domain network (MDNet) is proposed to solve this problem. By adding the Image-Align layer to the video target tracking task, a more accurate target value is obtained; applying the Directed Acyclic Graph-Recurrent Neural Network (DAG-RNN) by combining it with a convolutional neural network and modeling image neighborhood context dependencies for the target region to be tracked, we improve the problem that conventional networks only perform multi-layer extraction for the appearance features of the target, thus resulting in poor robustness to changes in target appearance; ROI Align layer is added after the convolution layer to speed up target feature extraction.

This paper proposes a human body targets tracking algorithm based on depth information under parallel binocular cameras. The key issue of pedestrian targets tracking in the parallel binocular cameras system is how to achieve the correspondence between the cameras. We get intrinsic and extrinsic parameters by camera calibration. Then, the foreground target region is got by Gaussian background difference based on current and background images, which is used to obtain the parallax images of human body target by using region matching. Finally, the locations and depth information of human bodies are obtained by the projection of the parallax information, which are used to track human targets by Kalman filter. The experiment results shows that the proposed algorithm has high accuracy and is feasible in the real world.