A UAV perspective based lightweight target detection and tracking algorithm for intelligent transportation

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.

In this paper, the research significance of infrared small target detection and tracking is briefly introduced. Secondly, the target detection and tracking algorithm is reviewed. The basic concepts and main methods of target detection and tracking technology are clarified. The deep learning algorithm is introduced, because it becomes more and more popular in target detection and tracking algorithm. Low and relatively complex background, the detection and tracking of infrared small targets is difficult and important.

Target detection and tracking represent key challenges facing miniature fixed-wing unmanned aerial vehicles (UAVs), particularly at high cruising speeds. Therefore, this paper proposes a vision-based target detection and tracking algorithm that systematically couples two mainstream methods, namely, you only look once (YOLO) and kernel correlation filter (KCF) algorithms. This combination enables small fixed-wing UAVs to achieve reliable target detection and rapid target tracking. A customized vision-guidance module is constructed to implement this algorithm, and a dual-thread execution mechanism is developed to ensure that the computational resources are used effectively. A miniature fixed-wing UAV experimental platform is also constructed and evaluated. Flight experiments are performed, and the results demonstrate that the developed algorithm can achieve satisfactory detection and tracking accuracy for stationary and moving ground targets in complex environments.

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.

In this paper, an embedded video monitoring system for intelligent control is presented. This presented system uses DaVinci-based high-speed TMS320DM6467 chip as hardware component and uses Embedded Linux Operating System as software component, in addition, it takes target detection and tracking algorithms as its core. So it can not only meet the requirements of real-time video processing and heavy computation by the ARM+DSP dual-core processors, but also meet the requirements of the intellectualization of video monitoring by the target detection and tracking algorithms.

Target detection and tracking in real-time video streaming is one of the core tasks in the field of computer vision, and its performance directly affects the effectiveness of intelligent surveillance, automatic driving and other applications. In this paper, a three-frame frame difference method incorporating Sobel operator is proposed to optimize the boundary extraction by combining morphological filtering. Meanwhile, the Mean Shift-based target tracking algorithm is improved by introducing a recognizable interferer model with a dual attention mechanism to enhance the tracking robustness. Experiments show that the improved inter-frame difference method achieves F-measure of 93.38%, 96.20% and 91.83% on Highway, Road and Highway2 datasets, respectively, and the detection frame rate is improved to 32 f/s. The improved Mean Shift algorithm improves the tracking accuracy by 2.67%-3.09% and the success rate by 0.98%-1.75% over the mainstream algorithm on the UAV123 and VOT2024 datasets. In addition, the multi-target tracking experiments achieve MOTA of 75.76% and 80.61% on the MOT16 and MOT17 datasets, respectively, validating the comprehensive performance advantages of the algorithms. This study provides an efficient and robust solution for target detection and tracking in real-time video streaming.

With the continuous deepening of economic globalization and the continuous development of Internet big data, the spread of martial arts culture has attracted people’s attention. The advent of the Internet big data era has brought new opportunities and media for the dissemination of martial arts culture but also brought certain risks and challenges. This paper firstly uses the literature method, logical analysis method, and related theories of communication, with the help of the SWOT strategic analysis method, to analyze the internal advantages and disadvantages faced by martial arts culture communication itself, as well as the external opportunities in the context of the Internet big data era and challenges. In order to realize the target detection and tracking task of Martial athletes, this paper proposes a target detection and tracking algorithm for Martial athletes. First, the algorithm constructs and trains a fully CNN-based athlete target detection model, which can detect the first frame of athlete targets in martial arts competitions or martial arts training videos. Experiments show that the athlete positioning information output by the detection model and the video frame sequence are jointly input into the athlete target tracking module based on neighborhood similarity, so as to realize the target tracking of martial arts athletes.

Objective The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Aiming at the characteristics of small targets, few samples, many occlusions, and strong camouflage in the battlefield environment, a target detection and tracking system based on multi-source sensor data is designed. The system is mainly composed of three parts: the unmanned aerial vehicle (UAV) platform, the ground display control platform (GDCP), and the offline algorithm training platform. The offline-trained target detection and tracking algorithm is deployed in the UAV-borne AI processing chip, which can conduct the real-time detection and tracking of typical targets in the battlefield and send the results and attitude information of UAVs to the GDCP. The system can realize real-time detection of typical battlefield targets and tracking control through the GDCP.

The UWB through-wall radar plays an unique role in disaster rescue operation, by providing the identification and location of the on-site survivors under fire, earthquake, collapse and other disaster conditions, as well as anti-terrorism and explosive ordnance disposal scenarios. However, the scenario of the disaster site may be complex, and the internal structure and furnishings inside the building could not be predicted in advance. A special scene with metal obstacles in the detection space is investigated in this paper. The UWB through wall radar test system and its composition is presented, and then the algorithms for human target adaptive motion filter detection and obstacle interference suppression are discussed. Finally, a tracking trace aggregation algorithm for strong offset targets is proposed. By analyzing the test data., the results show that the adaptive target detection method proposed in this paper could effectively eliminate the interference of metal obstacles, achieving enough signal-to-noise ratio and signal-to-clutter ratio. The improved point trace aggregation algorithm is also verified in target tracking test, achieving ideal tracking and detection results are obtained.

The parallel processing mode of Field Programmable Gate Array (FPGA) and the ability to perform multiple tasks repeatedly meet the real-time and flexible requirements of target detection and tracking. The FPGA of Cyclone IV of ALTERA company is adopted as the hardware processor for the system platform to filter and preprocess color conversion of the collected image. The edge detection method of Sobel operator is adopted to realize the convolution and binarization processing of the third-order matrix of pixel points and the operator template, and the inter-frame difference algorithm is applied to the moving target. At the same time, the tracking algorithm based on centroid feature matching can realize the real-time tracking of target position. The system uses the characteristics of high real-time, low power consumption, parallel processing and strong expansibility of the FPGA, transplants machine vision algorithm into the FPGA, reduces the delay of image processing, and completes the hardware implementation of the target detection and tracking algorithm. Finally, the experiment verifies the real-time performance and effectiveness of the algorithm.

This paper addresses two problems commonly associated with video target tracking system. First, video target detection and tracking usually require extensive searching in a large space to find the best matches for preregistered templates. Existing fast search methods cannot guarantee a global optimal match, which results in substandard performance. To obtain a true global match, a full search at the pixel or sub-pixel level is required. Obviously, this introduces significant computational overhead, which limits the implementation of these algorithms in real-time applications. In this paper, we propose a fast method to compute two-dimensional normalized cross-correlations to efficiently find the global optimal match result from a large image area. Comparisons and complexity analysis are provided to show the efficiency of the proposed algorithm. Second, another challenge commonly faced by detection and tracking systems is the accurate detection of target orientation in a twodimensional image. This problem is motivated by applications where the walk-in and walk-out people need to be detected and a fast image registration method is needed to compensate the change in rotation, translation and size, which is natural since the target's distance from the camera is changing dramatically. To address this issue, we propose a novel and efficient eigenvector-based method to detect target orientation and apply it into automatic human recognition system. Experimental and real-world test results verify that the proposed fast algorithm achieves similar accuracy as the recursive registration method which is computationally expensive.

针对图像序列中运动目标检测、跟踪的难点问题，提出了一种实时运动目标检测与跟踪算法。该算法基于自适应背景建模，获取运动目标背景模型和前景图像，从而实现运动目标检测；通过建立运动目标的位置、大小、形状以及颜色分布模型，构造运动目标全局匹配函数，结合目标活力特征，实现多运动目标连续匹配和跟踪。实验结果表明，相对于传统的运动目标跟踪方法，本文方法明显减少了运算时间，增强了环境适应性，实现了复杂场景下运动目标的准确检测和稳定跟踪，对非刚性目标的形变、旋转具有较强的鲁棒性。In this paper, we propose a real-time mobile target detection and tracking algorithm for challenges of mobile target detection and tracking in sequential images. This algorithm based on the adaptive background modeling obtains background model and front-view images of mobile targets, which is the way to achieve target detection. Continuous matching and tracking of multiple mobile targets are realized through constructing position, size, shape and color distribution of the mobile targets, defining a global matching function for those targets, and associating their vitality characteristics. It is demonstrated by experiments that the algorithm presented in this paper, compared to the traditional methods of mobile target tracking, significantly reduces the computation time, improves adaptive feature to environments, achieves accurate detection and robust tracking of mobile targets in complex environments, and shows strong robustness to deformation and rotation of non-rigid 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 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.