Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations

Integrated track maintenance for the PMHT via the hysteresis model

Copyright © 2007 IEEEUnlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor dataSamuel J. Davey; Douglas A. Gra

Singer memory filter data association for moving target tracking in heavy clutter.

праћење циљева / target trackingПредмет истраживања ове дисертације је поступак придруживања података у процесу праћења покретних циљева. У основи, познато је да сeнзoри oбeзбeђуjу мeрeњa, нa oснoву кojих сe мoжe извршити прoцeнa (eстимaциja) пaрaмeтaрa и стaњa систeмa. У вeћини случajeвa je тaчнo пoзнaт извoр мeрeњa тaкo дa сe прoцeнa врши стaндaрдним мeтoдaмa (Kalman-Bucy филтeр). Aсoциjaциja пoдaтaкa je нeoпхoднa у ситуaциjaмa кaдa ниje пoзнaт извoр мeрeњa, као што је oсмaтрaњe прoстoрa рaдaром или сoнaром. Рaдaр шaљe импулс eлeктрoмaгнeтскe eнeргиje и прoцeсирa примљeнe oдjeкe. Кaдa снaгa примљeнoг сигнaлa прeђe oдрeђeни прaг, дoлaзи дo дeтeкциje. Дeтeкциje сe прojeктуjу у oсмaтрaчкe кooрдинaтe кoje пoстajу улaзнa мeрeњa зa блoк прaћeњa циљeвa. Тa мeрeњa мoгу пoтицaти oд циљeвa, aли истo тaкo и oд случajних oбjeкaтa и фeнoмeнa. Извoр свaкoг мeрeњa je нeпoзнaт. Мeрeњa кoja пoтичу oд циљeвa (”тaчнa” мeрeњa) нису пoуздaнa, jeр су у свaкoм интeрвaл мeрeњa присутнa сaмo сa oдрeђeнoм вeрoвaтнoћoм дeтeкциje. Нeжeљeнa мeрeњa сe oбичнo зoву “клaтeр”, oнa сe пojaвљуjу случajнo, и oбичнo сe мoдeлирajу кao Пoaсoнoв прoцeс описан густинoм брoja клaтeр мeрeњa у прoстoру oсмaтрaњa...The subject of the research topic presented in this doctoral dissertation is data association of moving target tracking. Basically, it is known that sensors provide the measurement, which results can effectively be used for evaluation (estimation) of the parameters and state of the system. In most cases, the exact source of measurement is known, thus estimation is performed by using standard methods (Kalman-Bucy filter). Data association is necessary in scenarios where source of measurements is not known, such as the observation of radar or sonar. Radar sends a pulse of electromagnetic energy and processes the received echoes. When the power of the received signal exceeds a certain threshold, there is a detection. Detections are projected in the observation coordinates which then become input measurements for the target tracking system. This measurement may derive from the targets, but also from random objects and phenomena. Source of each measurement is unknown. Measurements derived from targets ("true" measurements) are not reliable, because in each measurement interval they are present only with a certain probability of detection. Adverse measurements are commonly called "clutter", they appear randomly, and are usually modeled as a Poisson process described Clutter number density measurements in space observation..

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Multitarget Tracking Menggunakan Multiple Hypothesis Tracking Dengan Clustering TIME Window Data Radar

Sistem radar dibagi menjadi dua jenis yaitu sistem radar sipil dan sistem radar militer. Kedua jenis sistem radar tersebut memiliki kesamaan yaitu telah digunakan untuk pemantauan lalu lintas udara. Pesawat yang dipantau di udara saat ini mengalami jumlah peningkatan yang besar sehingga untuk memudahkan pemantauannya diperlukan suatu sistem yang dinamakan multitarget aircraft tracking. Penelitian ini bertujuan untuk mendapatkan algoritma multitarget tracking (MTT) yang valid, yaitu dengan menggunakan kombinasi preprocessing data radar dengan clustering time window (CTW) dan algoritma Multiple Hypothesis Tracking (MHT).Penelitian ini diawali dengan penyiapan data rekaman radar yang direkam langsung. Kemudian data rekaman tersebut disimulasikan dengan algoritma yang telah dirancang. Pengecekan kemampuan algoritma tersebut dilakukan dengan membandingkannya dengan MHT tanpa clustering time window. Uji coba dilakukan dengan memakai data rekaman berdurasi kurang lebih 18 menit. Uji coba tersebut menghasilkan nilai correct target sebesar 87.66%, undetected target sebesar 12.81%, maintain target sebesar 80.5% dan inexisting target sebesar 23.65%. Dari hasil uji coba menunjukkan bahwa metode yang diusulkan lebih bagus jika dibandingkan dengan metode MHT tanpa CTW

Contextual information aided target tracking and path planning for autonomous ground vehicles

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion