Real-time people tracking in a camera network

Visual tracking is a fundamental key to the recognition and analysis of human behaviour. In this thesis we present an approach to track several subjects using multiple cameras in real time. The tracking framework employs a numerical Bayesian estimator, also known as a particle lter, which has been developed for parallel implementation on a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single tracking unit we represent the human body by a parametric ellipsoid in a 3D world. The elliptical boundary can be projected rapidly, several hundred times per subject per frame, onto any image for comparison with the image data within a likelihood model. Adding variables to encode visibility and persistence into the state vector, we tackle the problems of distraction and short-period occlusion. However, subjects may also disappear for longer periods due to blind spots between cameras elds of view. To recognise a desired subject after such a long-period, we add coloured texture to the ellipsoid surface, which is learnt and retained during the tracking process. This texture signature improves the recall rate from 60% to 70-80% when compared to state only data association. Compared to a standard Central Processing Unit (CPU) implementation, there is a signi cant speed-up ratio

Multi camera soccer player tracking

Now a day’s spread of super computers, existing of high resolution and low-priced video cameras, and increasing the computerized video analysis has made more curiosity in tracking algorithms. Automatic identification and tracing of multiple moving objects through video scene is an interesting field of computer visualization. Identification and tracking of multiple people is a vital and challenging task for many applications like human-computer interface, video communication, security application and surveillance system. Various researchers offer various algorithms but none of this was work properly to distinguish the players automatically when creating occlusion. The first step to tracking multiple objects in video sequence is detection. Background subtraction is a very popular and effective method for foreground detection (assuming that background should be stationary). In this thesis we apply various background subtraction methods to tackle the difficulties like changing illumination condition, background clutter and camouflage. The method we propose to overcome this problem is operates the background subtraction by calculating the Mahalanobis distances. The second step to track multiple moving objects in soccer scene by using particle filters method that estimate the non-Gaussian, non-linear state-space model, which is a multi-target tracking method. These methods are applied on real soccer video sequences and the result show that it is successfully track and distinguish the players. After tracking is done by using multi camera views, we collecting the data from all cameras and creating geometrical relationship between cameras called Homography

Tracking human motion with multiple cameras using articulated ICP with hard constraints

Questa tesi propone un nuovo algoritmo basato su ICP per il tracking di un modello scheletrico articolato di un corpo umano. L\u2019algoritmo proposto prende in input immagini calibrate di un soggetto, calcola la ricostruzione volumetrica e la linea mediale del corpo e quindi posiziona in modo adeguato il modello, composto di segmenti, in ogni frame usando una versione di ICP modificata (versione che usa una strategia di attraversamento alberi gerarchica che mantiene connessi tutti i segmenti del modello nei giunti relativi). L\u2019approccio proposto usa limiti cinematica per i giunti e un filtro di Kalman esteso per fare il tracking del modello. Il primo contributo originale di questa tesi \ue8 l\u2019algoritmo per trovare i punti sullo scheletro di un volume tridimensionale. L\u2019algoritmo, usando una tecnica di slicing trova l\u2019asse mediale di un volume 3D in modo veloce utilizzando il processore della scheda grafica e le texture units della scheda stessa. Questo algoritmo produce ottimi risultati per quanto riguarda la qualit\ue0 e le prestazioni se comparato con altri algoritmi in letteratura. Un altro contributo originale \ue8 l\u2019introduzione di una nuova strategia di tracking basata su un approccio gerarchico dell\u2019algoritmo ICP, utilizzato per trovare le congruenze tra un modello di corpo umano composto da soli segmenti e un insieme di punti 3D. L\u2019algoritmo usa una versione di ICP dove tutti i punti 3D sono pesati in funzione del segmento del corpo preso in considerazione dall\u2019algoritmo in quel momento. L\u2019applicazione di queste tecniche dimostra la bont\ue0 del metodo e le prestazioni ottenute in termini di qualit\ue0 della stima della posa sono comparabili con altri lavori in letteratura. I risultati presentati nella tesi dimostrano la fattibilit\ue0 dell\u2019approccio generale, che si intende utilizzare in un sistema completo per il tracking di corpi umani senza l\u2019uso di marcatori. In futuro il lavoro pu\uf2 essere esteso ottimizzando l\u2019implementazione e la codifica in modo da poter ottenere prestazioni real-time.This thesis proposed a new ICP-based algorithm for tracking articulated skeletal model of a human body. The proposed algorithm takes as input multiple calibrated views of the subject, computes a volumetric reconstruction and the centerlines of the body and fits the skeletal body model in each frame using a hierarchic tree traversal version of the ICP algorithm that preserves the connection of the segments at the joints. The proposed approach uses the kinematic constraints and an Extended Kalman Filter to track the body pose. The first contribution is a new algorithm to find the skeletal points of a 3D volume. The algorithm using a slicing technique find the medial axis of a volume in a fast way using the graphic card processor and the texture units. This algorithm produce good results in quality and performance compared to other works in literature. Another contribution is the introduction of a new tracking strategy based on a hierarchical application of the ICP standard algorithm to find the match between a stick body model and a set of 3D points. The algorithm use a traversing version of ICP where also all the 3D points are weighted in such a way every limbs of the model can best fit on the right portion of the body. The application of these techniques shown the feasibility of the method and the performances obtained in terms of quality of estimate pose are comparable with other works in literature. The results presented here demonstrate the feasibility of the approach, which is is intended to be used in complete system for vision-based markerless human body tracking. Future work will aimed at optimizing the implementation, in order to achieve real-time performances

3D VISUAL TRACKING USING A SINGLE CAMERA

automated surveillance and motion based recognition. 3D tracking address the localization of moving target is the 3D space. Therefore, 3D tracking requires 3D measurement of the moving object which cannot be obtained from 2D cameras. Existing 3D tracking systems use multiple cameras for computing the depth of field and it is only used in research laboratories. Millions of surveillance cameras are installed worldwide and all of them capture 2D images. Therefore, 3D tracking cannot be performed with these cameras unless multiple cameras are installed at each location in order to compute the depth. This means installing millions of new cameras which is not a feasible solution. This work introduces a novel depth estimation method from a single 2D image using triangulation. This method computes the absolute depth of field for any object in the scene with high accuracy and short computational time. The developed method is used for performing 3D visual tracking using a single camera by providing the depth of field and ground coordinates of the moving object for each frame accurately and efficiently. Therefore, this technique can help in transforming existing 2D tracking and 2D video analytics into 3D without incurring additional costs. This makes video surveillance more efficient and increases its usage in human life. The proposed methodology uses background subtraction process for detecting a moving object in the image. Then, the newly developed depth estimation method is used for computing the 3D measurement of the moving target. Finally, the unscented Kalman filter is used for tracking the moving object given the 3D measurement obtained by the triangulation method. This system has been test and validated using several video sequences and it shows good performance in term of accuracy and computational complexity

Multiple detections application for indoor tracking using PIR sensor and Kalman filter

Recently, human tracking in multiple indoor environments is getting broadly in demand to enhance security surveillance. Traditional passive video surveillance shown that it has working ineffectively nowadays because the number of cameras has exceeded the ability of operators to monitor them. In this paper, we proposed methods of detecting human presence using Pyroelectric Infrared (PIR) Motion Sensor and tracking people in multiple indoor locations using Kalman filter-based estimation. The proposed method is implemented to analyze the movement of people within the prescribed area and the result will be presented in footprint mapping of the said area. This will further enhanced building security surveillance especially at the sensitive or restricted areas. Experiments for single target tracking in several areas are carried out to verify the application of the developed system. As the results, the maximum error for tracking trajectory reduced from 0.28m to 0.19m and average error for tracking trajectory also reduced from 0.10m to 0.07m after using Kalman filter estimation algorithm

Path planning for socially-aware humanoid robots

Designing efficient autonomous navigation systems for mobile robots involves consideration of the robotís environment while arriving at a systems architecture that trades off multiple constraints. We have architected a navigation framework for socially-aware autonomous robot navigation, using only the on-board computing resources. Our goal is to foster the development of several important service robotics applications using this platform. Our framework allows a robot to autonomously navigate in indoor environments while accounting for people (i.e., estimating the path of all individuals in the environment), respecting each individualís private space. In our design, we can leverage a wide number of sensors for navigation, including cameras, 2D and 3D scanners, and motion trackers. When designing our sensor system, we have considered that mobile robots have limited resources (i.e., power and computation) and that some sensors are costlier than others (e.g., cameras and 3D scanners stream data at high rates), requiring intensive computation to provide useful insight for real-time navigation. We tradeoff between accuracy, responsiveness, and power, and choose a Hokuyo UST-20LX 2D laser scanner for robot localization, obstacle detection and people tracking. We use an MPU-6050 for motion tracking. Our navigation framework features a low-power sensor system (< 5W) tailored for improved battery life in robotic applications while providing sufficient accuracy. We have completed a prototype for a Human Support Robot using the available onboard computing devices, requiring less than 60W to run. We estimate we can obtain similar performance, while reducing power by ~60%, utilizing low-power high-performance accelerator hardware and parallelized software.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec