Human target detection and tracking under parallel binocular cameras

Radar based detection of human targets behind walls or in dense urban environments is an important technical challenge with many practical applications in security, defense, and disaster recovery. Radar reflections from a human can be orders of magnitude weaker than those from objects encountered in urban settings such as walls, cars, or possibly rubble after a disaster. Furthermore, these objects can act as secondary reflectors and produce multipath returns from a person. To mitigate these issues, processing of radar return data needs to be optimized for recognizing human motion features such as walking, running, or breathing. This paper presents a theoretical analysis on the modulation effects human motion has on the radar waveform and how high levels of multipath can distort these motion effects. From this analysis, an algorithm is designed and optimized for tracking human motion in heavily clutter environments. The tracking results will be used as the fundamental detection/classification tool to discriminate human targets from others by identifying human motion traits such as predictable walking patterns and periodicity in breathing rates. The theoretical formulations will be tested against simulation and measured data collected using a low power, portable see-through-the-wall radar system that could be practically deployed in real-world scenarios. Lastly, the performance of the algorithm is evaluated in a series of experiments where both a single person and multiple people are moving in an indoor, cluttered environment.

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two‐dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root‐mean‐square error pruning algorithm is proposed for multi‐target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage‐penetration environment.

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.

Blind estimation of number of motion multi-human targets in wireless pyroelectric infrared sensor networks

Multistatic ultra-wideband radar is acknowledged to be useful for human tracking in indoor surveillance. In this thesis, a global signal processing chain for detection and localization of moving human targets using a multistatic ultra-wideband radar system is proposed. The design options for the system are presented, and the required signal processing steps are summarized. They include critical steps of tracking, target feature extraction,and target association. A decentralized tracking approach is designed for indoor surveillance. The 1D bistatic tracking rejects clutter and multipath significantly. The 2D tracker employs the global nearest neighbor based Kalman filter to track targets in Cartesian coordinates. The algorithm performance is compared with that of the conventional centralized approach on both simulated and experimental data, and shown to improve clutter and multipath rejection. For feature extraction, the advantages of using range-Doppler imaging are analyzed. The hypothesis-testing based and the Keystone transform based range migration compensation approaches are extended to eliminate the Doppler widening effect in range-Doppler processing of ultra-wideband radar. The proposed compensation approaches are applied to both simulated and experimental data. Special attention is then devoted to the critical issue of target association. Two slow-time features, video time density function and self-similarity matrix, are proposed to characterize target responses in different receiving channels. They indicate the slow-time evolution characteristic of the moving human targets, while staying invariant to radar observation angles. Auction algorithm based global nearest neighbor approach is proposed to tackle the measurement-to-measurement association issue. The decentralized tracking approach is experimentally verified on measured data for indoor detection and location of multiple human targets. Finally, as a by-product of human target feature analysis, range-Doppler surface is proposed to demonstrate the overall target information in range-Doppler video sequence. The conclusion emphasizes the main achievements and highlights the novelties. The recommendation for future work is presented finally to point out potential research topics related to this thesis.

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.

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

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

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.

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.

Soccer player video target tracking is a very challenging task, which has good practical and commercial value. Traditional soccer game target tracking relies on athletes to carry a recording chip to achieve target tracking, but the cost is very high. With the rapid development of photography technology and deep learning technology, athletes’ target tracking is realized through soccer game video. Deep learning technology is applied to computer vision detection and tracking. How to realize soccer players’ video target tracking under deep learning is a challenging lesson. To solve this problem, this paper takes the video target tracking of football players as the research object, collects the game images of the stadium through multiple cameras, realizes the long-term accurate tracking of multiple players, and establishes a multicamera multitarget tracking system. The KCF algorithm and the improved KCF algorithm formed by replacing the hog feature of the KCF algorithm with the depth convolution neural network are used to compare and analyze the impact of different target tracking ranges and target numbers on the target tracking accuracy of the system, so as to accurately obtain the motion trajectory of football players. The results show that the image data of football matches are collected independently by multiple cameras, and the data of multiple cameras are collected to generate each target motion datum. The KCF algorithm of multicamera multitarget tracking has good robustness and real-time for long-term accurate tracking of football players; the KCF algorithm and the improved KCF algorithm have high accuracy in target tracking. With the increase of tracking frame range, the accuracy of target tracking of the two algorithms is improved. At the same time, multitarget tracking helps to improve the antiocclusion ability of the system. The research results have important practical significance and good application prospects for the analysis technology of video content of football matches.

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

An improved outdoor camera calibration method based on Extended Kalman Filter (EKF) is proposed in this paper. GPS coordinate system is introduced to establish world coordinate system in the calibration procedure. According to the characteristics of GPS coordinate converted to plane coordinate and the camera calibration model, we establish the mapping relations of reference points between image plane coordinate and the world coordinate. Further, we consider the world coordinate error, set the world coordinates as the measure quantities, use the method of EKF to get the optimal estimation of intrinsic and extrinsic camera parameters, in EKF the camera's intrinsic and extrinsic parameters are set as state variables, the transformation equations of image coordinate to the world coordinate are set as the measurement equations, and we let the observed feature points' image coordinates and corresponding world coordinates as the input of the filter. Experimental results confirmed that the proposed method with high precision. Index Terms - camera calibration, GPS coordinate, EKF, world coordinate error

Multistatic active target tracking in GPS-denied scenarios is complicated by the fact that the emitter and receiver locations are unknown and must be estimated jointly with the target track. Maximum a posteriori algorithms for solving this joint estimation problem are complicated by the nonlinearities in the likelihood function of the bistatic range measurement. A new integral representation of this likelihood function is presented for small measurement error variances. Remarkably, target state appears linearly in this integral. This paper presents a new approach to the basic problem of target state estimation for known sensor locations. The optimal estimator derived from the integral representation is an iteratively re-weighted linear Kalman smoother. Joint estimators for target and emitter-receiver field tracking will be reported elsewhere.