Particle Filter with Gaussian Weighting for Human Tracking

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weight. Particle filter with Gaussian weighting is proposed to accomplish the computational problem. There are two main stages in this method, i.e. prediction and update. The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage. In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle’s weight is calculated, and the remain particle’s weight is calculated using the Gaussian weighting. Experiment is done using artificial dataset. The average accuracy is 80,862%. The high accuracy that is achieved by this method could use for the real-time system trackin

Real-time, long-term hand tracking with unsupervised initialization

This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods

SIMULASI DAN ANALISIS MULTIPLE OBJECT TRACKING BERBASIS CITRA DENGAN METODE HIERARCHICAL PARTICLE FILTER

ABSTRAKSI: Objek tracking merupakan suatu bidang pada computer vision yang mempelajari cara melacak suatu objek yang bergerak pada suatu ruang. Objek yang dilacak merupakan objek yang sudah ditentukan. Pelacakan suatu objek bergerak sangat berguna untuk membantu tugas penting dalam aplikasi komputer vision seperti: pengenalan gerakan, pelacakan kendaraan, penghitungan jumlah kendaraan, augmented reality dan video kompresi. Object tracking mempunyai beberapa masalah antara lain adanya noise, kekacauan oklusi, dan perubahan dinamis dalam gerakan objek. Sehingga pada tugas akhir ini, dirancang sebuah sistem multiple object tracking dengan metode particle filter. Particle filter, juga dikenal sebagai sequential Monte Carlo merupakan salah satu metode stokastik yang telah dikembangkan dalam suatu komunitas visi komputer dan diterapkan untuk masalah pelacakan.Sistem pelacakaan ini bekerja dengan masukan secara non-real time. Objek yang dilacak berupa manusia. Proses pendeteksian manusia menggunakan metode Histogram of Oriented Gradient. Setelah melakukan deteksi manusia, maka objek tersebut akan dilacak dengan metode particle filter. Proses pelacakan dilakukan dengan cara membangkitkan random partikel pada area dekat dengan objek. Selanjutnya dilakukan proses model observasi untuk menghitung kemungkinan dari partikel tersebut yang mempunyai kesamaan histogram dengan objek target. Penghitungan kesamaan dilakukan dengan menggunakan Bhattacharyya coefficient.Pengujian pada sistem ini dengan menggunakan parameter yang diubah-ubah yaitu threshold maksimum, threshold minimum, jumlah partikel, jumlah objek dan kondisi perekaman objek. Tingkat akurasi terbaik pada threshold 0.88 dan threshold minimum 0.73. Jumlah objek pada proses pelacakan menggunakan particle filter berbanding lurus dengan waktu pemrosesan, sedangkan pada pelacakan dengan metode Histogram of Oriented Gradient berbanding lurus dengan resolusi video.Kata Kunci : object tracking, histogram, Bhattacharyya coefficient, Histogram of Oriented Gradient, particle filter.ABSTRACT: Object tracking is a field in computer vision that learn how to track a moving object in a space. Tracked object is an object that has been determined. Tracking a moving object is very useful to help an important task in computer vision applications such as: the introduction of motion, vehicle tracking, vehicle counts, augmented reality and video compression. Object tracking has many problems such as the noise, clutter occlusion, and dynamic changes in the motion of the object. So that the final project, designed a multiple object tracking system with particle filter method. Particle filters, also known as sequential Monte Carlo is a stochastic method that has been developed in the computer vision community and applied to tracking problems.Tracking’s system works by using video processing in a non-real time. Tracked object in the form of humans. In the human detection process, use the Histogram of Oriented Gradient method. After the detection of human doing, then the object will be tracked with a particle filter method. Tracking process is done by generating random particles at close to the object area. Then performed the observations made models to calculate the probability of a particle that has the same histogram as the target object. Similarity calculation is done by using the Bhattacharyya coefficient. Then made a new estimate of the object\u27s position.Testing the system using modified parameters namely maximum threshold, the minimum threshold, the number of particles, the number of objects and the recording condition of the object. Best accuracy rate at 0.88 threshold and the minimum threshold of 0.73. Total objects in the tracking process using the particle filter is directly proportional to the processing time, whereas in the tracking Histogram of Oriented Gradient method proportional to the resolution of the video.Keyword: object tracking, histogram, Bhattacharyya coefficient, Histogram of Oriented Gradient, particle filters

Video analytics for security systems

This study has been conducted to develop robust event detection and object tracking algorithms that can be implemented in real time video surveillance applications. The aim of the research has been to produce an automated video surveillance system that is able to detect and report potential security risks with minimum human intervention. Since the algorithms are designed to be implemented in real-life scenarios, they must be able to cope with strong illumination changes and occlusions. The thesis is divided into two major sections. The first section deals with event detection and edge based tracking while the second section describes colour measurement methods developed to track objects in crowded environments. The event detection methods presented in the thesis mainly focus on detection and tracking of objects that become stationary in the scene. Objects such as baggage left in public places or vehicles parked illegally can cause a serious security threat. A new pixel based classification technique has been developed to detect objects of this type in cluttered scenes. Once detected, edge based object descriptors are obtained and stored as templates for tracking purposes. The consistency of these descriptors is examined using an adaptive edge orientation based technique. Objects are tracked and alarm events are generated if the objects are found to be stationary in the scene after a certain period of time. To evaluate the full capabilities of the pixel based classification and adaptive edge orientation based tracking methods, the model is tested using several hours of real-life video surveillance scenarios recorded at different locations and time of day from our own and publically available databases (i-LIDS, PETS, MIT, ViSOR). The performance results demonstrate that the combination of pixel based classification and adaptive edge orientation based tracking gave over 95% success rate. The results obtained also yield better detection and tracking results when compared with the other available state of the art methods. In the second part of the thesis, colour based techniques are used to track objects in crowded video sequences in circumstances of severe occlusion. A novel Adaptive Sample Count Particle Filter (ASCPF) technique is presented that improves the performance of the standard Sample Importance Resampling Particle Filter by up to 80% in terms of computational cost. An appropriate particle range is obtained for each object and the concept of adaptive samples is introduced to keep the computational cost down. The objective is to keep the number of particles to a minimum and only to increase them up to the maximum, as and when required. Variable standard deviation values for state vector elements have been exploited to cope with heavy occlusion. The technique has been tested on different video surveillance scenarios with variable object motion, strong occlusion and change in object scale. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. Tracking objects in a multi camera environment is discussed in the final part of the thesis. The ASCPF technique is deployed within a multi-camera environment to track objects across different camera views. Such environments can pose difficult challenges such as changes in object scale and colour features as the objects move from one camera view to another. Variable standard deviation values of the ASCPF have been utilized in order to cope with sudden colour and scale changes. As the object moves from one scene to another, the number of particles, together with the spread value, is increased to a maximum to reduce any effects of scale and colour change. Promising results are obtained when the ASCPF technique is tested on live feeds from four different camera views. It was found that not only did the ASCPF method result in the successful tracking of the moving object across different views but also maintained the real time frame rate due to its reduced computational cost thus indicating that the method is a potential practical solution for multi camera tracking applications

Realtime tracking and grasping of a moving object from range video

In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in real-time and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.Peer ReviewedPostprint (author’s final draft

Realtime tracking and grasping of a moving object from range video

Presentado al ICRA 2014 celebrado en Hong Kong del 31 de mayo al 7 de junio.In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in real-time and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.This work was supported by the EU project IntellAct FP7-269959, the project PAU+ DPI2011-27510 and the project CINNOVA 201150E088. B. Dellen was supported by the Spanish Ministry for Science and Innovation via a Ramon y Cajal fellowship.Peer Reviewe

Visual tracking: detecting and mapping occlusion and camouflage using process-behaviour charts

Visual tracking aims to identify a target object in each frame of an image sequence. It presents an important scientific problem since the human visual system is capable of tracking moving objects in a wide variety of situations. Artificial visual tracking systems also find practical application in areas such as visual surveillance, robotics, biomedical image analysis, medicine and the media. However, automatic visual tracking algorithms suffer from two common problems: occlusion and camouflage. Occlusion arises when another object, usually with different features, comes between the camera and the target. Camouflage occurs when an object with similar features lies behind the target and makes the target invisible from the camera’s point of view. Either of these disruptive events can cause a tracker to lose its target and fail. This thesis focuses on the detection of occlusion and camouflage in a particle-filter based tracking algorithm. Particle filters are commonly used in tracking. Each particle represents a single hypothesis as to the target’s state, with some probability of being correct. The collection of particles tracking a target in each frame of an image sequence is called a particle set. The configuration of that particle set provides vital information about the state of the tracker. The work detailed in this thesis presents three innovative approaches to detecting occlusion and/or camouflage during tracking by evaluating the fluctuating behaviours of the particle set and detecting anomalies using a graphical statistical tool called a process-behaviour chart. The information produced by the process-behaviour chart is then used to map out the boundary of the interfering object, providing valuable information about the viewed environment. A method based on the medial axis of a novel representation of particle distribution termed the Particle History Image was found to perform best over a set of real and artificial test sequences, detecting 90% of occlusion and 100% of camouflage events. Key advantages of the method over previous work in the area are: (1) it is less sensitive to false data and less likely to fire prematurely; (2) it provides a better representation of particle set behaviour by aggregating particles over a longer time period and (3) the use of a training set to parameterise the process-behaviour charts means that comparisons are being made between measurements that are both made over extended time periods, improving reliability