3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

The effects of TMS over dorsolateral prefrontal cortex on trans-saccadic memory of multiple objects

Humans typically make several rapid eye movements (saccades) per second. It is thought that visual working memory can retain and spatially integrate three to four objects or features across each saccade but little is known about this neural mechanism. Previously we showed that transcranial magnetic stimulation (TMS) to the posterior parietal cortex and frontal eye fields degrade trans-saccadic memory of multiple object features (Prime, Vesia, & Crawford, 2008, Journal of Neuroscience, 28(27), 6938-6949; Prime, Vesia, & Crawford, 2010, Cerebral Cortex, 20(4), 759-772.). Here, we used a similar protocol to investigate whether dorsolateral prefrontal cortex (DLPFC), an area involved in spatial working memory, is also involved in trans-saccadic memory. Subjects were required to report changes in stimulus orientation with (saccade task) or without (fixation task) an eye movement in the intervening memory interval. We applied single-pulse TMS to left and right DLPFC during the memory delay, timed at three intervals to arrive approximately 100ms before, 100ms after, or at saccade onset. In the fixation task, left DLPFC TMS produced inconsistent results, whereas right DLPFC TMS disrupted performance at all three intervals (significantly for presaccadic TMS). In contrast, in the saccade task, TMS consistently facilitated performance (significantly for left DLPFC/perisaccadic TMS and right DLPFC/postsaccadic TMS) suggesting a dis-inhibition of trans-saccadic processing. These results are consistent with a neural circuit of trans-saccadic memory that overlaps and interacts with, but is partially separate from the circuit for visual working memory during sustained fixation.

Grid-based method for ranking images with multiple objects

One of the content-based image retrieval techniques is the shape-based technique, which allows users to ask for objects similar in shape to a query object. Sajjanhar and Lu proposed a method for shape representation and similarity measure called the grid-based method [1]. They have shown that the method is effective for the retrieval of segmented objects based on shape. In this paper, we describe a system which uses the grid-based method for retrieval of images with multiple objects. We perform experiments on the prototype system to compare the performance of the grid-based method with the Fourier descriptors method [2]. Preliminary results have been presented.

Design of a 3D printing capable of creating multiple objects simultaneously

Proyecto de Fin de Carrera de Ingenieria Industrial, versando sobre el completo diseño de una impresora 3D

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Safety enhancement of operator protection systems on self-propelled mining equipment

In the long term, with development of skill, knowledge, exposure and confidence within the engineering profession, rigorous analysis techniques have the potential to become a reliable and far more comprehensive method for design and verification of the structural adequacy of OPS, write Nimal J Perera, David P Thambiratnam and Brian Clark. This paper explores the potential to enhance operator safety of self-propelled mechanical plant subjected to roll over and impact of falling objects using the non-linear and dynamic response simulation capabilities of analytical processes to supplement quasi-static testing methods prescribed in International and Australian Codes of Practice for bolt on Operator Protection Systems (OPS) that are post fitted. The paper is based on research work carried out by the authors at the Queensland University of Technology (QUT) over a period of three years by instrumentation of prototype tests, scale model tests in the laboratory and rigorous analysis using validated Finite Element (FE) Models. The FE codes used were ABAQUS for implicit analysis and LSDYNA for explicit analysis. The rigorous analysis and dynamic simulation technique described in the paper can be used to investigate the structural response due to accident scenarios such as multiple roll over, impact of multiple objects and combinations of such events and thereby enhance the safety and performance of Roll Over and Falling Object Protection Systems (ROPS and FOPS). The analytical techniques are based on sound engineering principles and well established practice for investigation of dynamic impact on all self propelled vehicles. They are used for many other similar applications where experimental techniques are not feasible.

Reconstructing the properties of dark energy using standard sirens

Future space-based gravity wave (GW) experiments such as the Big Bang Observatory (BBO), with their excellent projected, one sigma angular resolution, will measure the luminosity distance to a large number of GW sources to high precision, and the redshift of the single galaxies in the narrow solid angles towards the sources will provide the redshifts of the gravity wave sources. One sigma BBO beams contain the actual source in only 68% of the cases; the beams that do not contain the source may contain a spurious single galaxy, leading to misidentification. To increase the probability of the source falling within the beam, larger beams have to be considered, decreasing the chances of finding single galaxies in the beams. Saini et al. T.D. Saini, S.K. Sethi, and V. Sahni, Phys. Rev. D 81, 103009 (2010)] argued, largely analytically, that identifying even a small number of GW source galaxies furnishes a rough distance-redshift relation, which could be used to further resolve sources that have multiple objects in the angular beam. In this work we further develop this idea by introducing a self-calibrating iterative scheme which works in conjunction with Monte Carlo simulations to determine the luminosity distance to GW sources with progressively greater accuracy. This iterative scheme allows one to determine the equation of state of dark energy to within an accuracy of a few percent for a gravity wave experiment possessing a beam width an order of magnitude larger than BBO (and therefore having a far poorer angular resolution). This is achieved with no prior information about the nature of dark energy from other data sets such as type Ia supernovae, baryon acoustic oscillations, cosmic microwave background, etc. DOI:10.1103/PhysRevD.87.083001

A MatLAB Based Virtual Phantom for 2D Electrical Impedance Tomography (MatVP2DEIT): Studying the Medical Electrical Impedance Tomography Reconstruction in Computer

Electrical Impedance Tomography (EIT) is a computerized medical imaging technique which reconstructs the electrical impedance images of a domain under test from the boundary voltage-current data measured by an EIT electronic instrumentation using an image reconstruction algorithm. Being a computed tomography technique, EIT injects a constant current to the patient's body through the surface electrodes surrounding the domain to be imaged (Omega) and tries to calculate the spatial distribution of electrical conductivity or resistivity of the closed conducting domain using the potentials developed at the domain boundary (partial derivative Omega). Practical phantoms are essentially required to study, test and calibrate a medical EIT system for certifying the system before applying it on patients for diagnostic imaging. Therefore, the EIT phantoms are essentially required to generate boundary data for studying and assessing the instrumentation and inverse solvers a in EIT. For proper assessment of an inverse solver of a 2D EIT system, a perfect 2D practical phantom is required. As the practical phantoms are the assemblies of the objects with 3D geometries, the developing of a practical 2D-phantom is a great challenge and therefore, the boundary data generated from the practical phantoms with 3D geometry are found inappropriate for assessing a 2D inverse solver. Furthermore, the boundary data errors contributed by the instrumentation are also difficult to separate from the errors developed by the 3D phantoms. Hence, the errorless boundary data are found essential to assess the inverse solver in 2D EIT. In this direction, a MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate boundary data for assessing the 2D-EIT inverse solvers and the image reconstruction accuracy. MatVP2DEIT is a MatLAB-based computer program which simulates a phantom in computer and generates the boundary potential data as the outputs by using the combinations of different phantom parameters as the inputs to the program. Phantom diameter, inhomogeneity geometry (shape, size and position), number of inhomogeneities, applied current magnitude, background resistivity, inhomogeneity resistivity all are set as the phantom variables which are provided as the input parameters to the MatVP2DEIT for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. Boundary data sets are generated with different phantom configurations obtained with the different combinations of the phantom variables and the resistivity images are reconstructed using EIDORS. Boundary data of the virtual phantoms, containing inhomogeneities with complex geometries, are also generated for different current injection patterns using MatVP2DEIT and the resistivity imaging is studied. The effect of regularization method on the image reconstruction is also studied with the data generated by MatVP2DEIT. Resistivity images are evaluated by studying the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed phantom domain. Results show that the MatVP2DEIT generates accurate boundary data for different types of single or multiple objects which are efficient and accurate enough to reconstruct the resistivity images in EIDORS. The spatial resolution studies show that, the resistivity imaging conducted with the boundary data generated by MatVP2DEIT with 2048 elements, can reconstruct two circular inhomogeneities placed with a minimum distance (boundary to boundary) of 2 mm. It is also observed that, in MatVP2DEIT with 2048 elements, the boundary data generated for a phantom with a circular inhomogeneity of a diameter less than 7% of that of the phantom domain can produce resistivity images in EIDORS with a 1968 element mesh. Results also show that the MatVP2DEIT accurately generates the boundary data for neighbouring, opposite reference and trigonometric current patterns which are very suitable for resistivity reconstruction studies. MatVP2DEIT generated data are also found suitable for studying the effect of the different regularization methods on reconstruction process. Comparing the reconstructed image with an original geometry made in MatVP2DEIT, it would be easier to study the resistivity imaging procedures as well as the inverse solver performance. Using the proposed MatVP2DEIT software with modified domains, the cross sectional anatomy of a number of body parts can be simulated in PC and the impedance image reconstruction of human anatomy can be studied.

VGIS-COLLIDE: an effective collision detection algorithm for

Collision detection is an important component in simulation applications which are based on virtual geographic information system (VGIS). In this paper, an effective collision detection algorithm for multiple objects in VGIS, VGIS-COLLIDE, is presented. The algorithm firstly integrates existing quadtree, which is the global hierarchical structure of VGIS, with axis-aligned bounding box of object to perform the broad-phase of collision detection. After that, exact collision detection between two objects which have passed the broad-phase of collision detection is performed. The algorithm makes no assumption about input primitives or object's motion and is directly applicable to all triangulated models. It can be applicable to both rigid and deformable objects without preprocessing. The performance of the algorithm has been demonstrated in several environments consisting of a high number of objects with hundreds of thousands of triangles.

Multicamera sport player tracking with Bayesian estimation of measurements

We propose a complete application capable of tracking multiple objects in an environment monitored by multiple cameras. The system has been specially developed to be applied to sport games, and it has been evaluated in a real association-football stadium. Each target is tracked using a local importance-sampling particle filter in each camera, but the final estimation is made by combining information from the other cameras using a modified unscented Kalman filter algorithm. Multicamera integration enables us to compensate for bad measurements or occlusions in some cameras thanks to the other views it offers. The final algorithm results in a more accurate system with a lower failure rate. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3114605]

Comparison of preferences for object properties in the rat using paired- and free-choice paradigms

A common method for testing preference for objects is to determine which of a pair of objects is approached first in a paired-choice paradigm. In comparison, many studies of preference for environmental enrichment (EE) devices have used paradigms in which total time spent with each of a pair of objects is used to determine preference. While each of these paradigms gives a specific measure of the preference for one object in comparison to another, neither method allows comparisons between multiple objects simultaneously. Since it is possible that several EE objects would be placed in a cage together to improve animal welfare, it is important to determine measures for rats' preferences in conditions that mimic this potential home cage environment. While it would be predicted that each type of measure would produce similar rankings of objects, this has never been tested empirically. In this study, we compared two paradigms: EE objects were either presented in pairs (paired-choice comparison) or four objects were presented simultaneously (simultaneous presentation comparison). We used frequency of first interaction and time spent with each object to rank the objects in the paired-choice experiment, and time spent with each object to rank the objects in the simultaneous presentation experiment. We also considered the behaviours elicited by the objects to determine if these might be contributing to object preference. We demonstrated that object ranking based on time spent with objects from the paired-choice experiment predicted object ranking in the simultaneous presentation experiment. Additionally, we confirmed that behaviours elicited were an important determinant of time spent with an object. This provides convergent evidence that both paired choice and simultaneous comparisons provide valid measures of preference for EE objects in rats. (C) 2007 Elsevier B.V. All rights reserved.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.