Stereo-vision-based moving object tracking via robust linear filtering

Vision-based tracking sensors typically provide nonlinear measurements
of the targets Cartesian position and velocity state components. In this paper we derive linear measurements using an analytical measurement conversion technique which can be used with two (or more) vision sensors. We derive
linear measurements in the target’s Cartesian position and velocity components and we derive a robust version of a linear Kalman filter. We show that our linear robust filter significantly outperforms the extended Kalman Filter. Moreover, we prove that the state estimation error is bounded.

Multiple-object tracking in cluttered and crowded public spaces

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Secure object tracking protocol for networked RFID systems

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

Secure object tracking protocol for the internet of things

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.

Object focused simultaneous estimation of optical flow and state dynamics

The framework of differential optical flow has been built upon to enhance the performance of motion estimation from optical flow. By coupling optical flow and object state parameters, an effective procedure for object tracking is implemented with the dasiaSimultaneous Estimation of Optical Flow and Object Statepsila (SEOS) technique. The SEOS method utilizes dynamic object parameter information when calculating optical flow for tracking a moving object within a video stream. Optical flow estimation for the SEOS method requires minimization of an error functional containing object physical parameter data. The convergence of an energy functional to a feasible or optimal solution set is not guaranteed. Convergence criteria is often assumed and not shown explicitly. Convergence of the SEOS method for both the Jacobi and Gauss-Seidel numerical resolution methods is evaluated.

Combining multiple tracking algorithms for improved general performance

Automated tracking of objects through a sequence of images has remained one of the difficult problems in computer vision. Numerous algorithms and techniques have been proposed for this task. Some algorithms perform well in restricted environments, such as tracking using stationary cameras, but a general solution is not currently available. A frequent problem is that when an algorithm is refined for one application, it becomes unsuitable for other applications. This paper proposes a general tracking system based on a different approach. Rather than refine one algorithm for a specific tracking task, two tracking algorithms are employed, and used to correct each other during the tracking task. By choosing the two algorithms such that they have complementary failure modes, a robust algorithm is created without increased specialisation.

Tracking in a space variant active vision system

Without the ability to foveate on and maintain foveation, active vision for applications such as surveillance, object recognition and object tracking are difficult to build. Although foveation in cartesian coordinates is being actively pursued by many, multi-resolution high accuracy foveation in log polar space has not been given much attention. This paper addresses the use of foveation to track a single object as well as multiple objects for a simulated space variant active vision system. Complex logarithmic mapping is chosen firstly because it provides high resolution and wide angle viewing. Secondly, the spatially variant structure of log polar space leads to an object increasing in size as it moves towards the fovea. This is important as we know which object is closer to the fovea at any instant in time.

LGT/VOT tracking performance evaluation of depth images

This paper presents object tracking in depth, RGB and normal-maps images using LGT tracker. The depth and RGB images are rendered using depth imaging plugins. A series of experiments were held to evaluate the tracker performance in tracking objects in different image sequences. The experiments conducted were from the Visual Object Tracking (VOT) challenge that was arranged in association with ICCV'13 The accuracy was chosen as the evaluation measure, where the the tracker's bounding box was compared against the ground truth bounding box. Results show that tracking object using depth images gives better results and is more accurate than tracking using either the RGB or nomal maps images.

Robust video/ultrasonic fusion-based estimation for automotive applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.

Wireless sensor networks for heritage object deformation detection and tracking algorithm

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.

An active stereo vision-based learning approach for robotic tracking, fixating and grasping control

In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.

Simultaneous estimation of optical flow and object state: A modified approach to optical flow calculation

Differential optical flow methods are widely used within the computer vision community. They are classified as being either local, as in the Lucas-Kanade method, or global, as in the Horn-Schunck technique. As the physical dynamics of an object is inherently coupled into the behavior of its image in the video stream, in this paper, we use such dynamic parameter information in calculating optical flow when tracking a moving object using a video stream. Indeed, we use a modified error function in the minimization that contains physical parameter information. Further, the refined estimates of optical flow is used for better estimation of the physical parameters of the object in the simultaneous estimation of optical flow and object state(SEOS).

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

A method for stereo-vision-based tracking for robotic applications

Vision-based tracking of an object using perspective projection inherently results in non-linear measurement equations in the Cartesian coordinates. The underlying object kinematics can be modelled by a linear system. In this paper we introduce a measurement conversion technique that analytically transforms the non-linear measurement equations obtained from a stereo-vision system into a system of linear measurement equations.We then design a robust linear filter around the converted measurement system. The state estimation error of the proposed filter is bounded and we provide a rigorous theoretical analysis of this result. The performance of the robust filter developed in this paper is demonstrated via computer simulation and via practical experimentation using a robotic manipulator as a target. The proposed filter is shown to outperform the extended Kalman filter (EKF).