Object Tracking

Video event detection (VED) is a challenging task especially with a large variety of objects in the environment. Even though there exist numerous algorithms for event detection, most of them are unsuitable for a typical consumer purpose. A hybrid method for detecting and identifying the moving objects by their color and spatial information is presented in this paper. In tracking multiple moving objects, the system makes use of motion of changed regions. In this approach, first, the object detector will look for the existence of objects that have already been registered. Then the control is passed on to an event detector which will wait for an event to happen which can be object placement or object removal. The object detector becomes active only if any event is detected. Simple training procedure using a single color camera in HSV color space makes it a consumer application. The proposed model has proved to be robust in various indoor environments and different types of background scenes. The experimental results prove the feasibility of the proposed method.

This paper presents a combination of techniques which recognize and categorize observed rigid objects, and compute their 3D pose with respect to an industrial robot's workspace frame. This allows an industrial robot to simultaneously recognize and follow the displacements of a specific rigid object, among other objects that may coexist in the same environment.

In this paper, a new algorithm for Automatic License Plate Localisation and Recognition (ALPR) is proposed on the basis of isotropic dilation that can be achieved using the binary image Euclidean distance transform. In a blob analysis problem, any two Region of Interest (RoIs) that is discontinuous are typically treated as separate blobs. However, the proposed algorithm combine with Connected Component Analysis (CCA) are coded to seek for RoI within a certain distance of other RoI to be treated as non-unique. This paper investigates the design and implementation of several pre-processing techniques and isotropic dilation algorithm to classify moving vehicles with different backgrounds and varying angles. A multi-layer feed-forward back-propagation Neural Network is used to train the segmented and refined characters. The results obtained can be used for implementation in the vehicle parking management system.

In this paper, we present two new articulated motion analysis and object tracking approaches: the decentralized articulated object tracking method and the hierarchical articulated object tracking method. The first approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking. Instead, we introduce a decentralized scheme and model the interpart interaction within an innovative Bayesian framework. Specifically, we estimate the interaction density by an efficient decomposed interpart interaction model. To handle severe self-occlusions, we further extend the first approach by modeling high-level interunit interaction and develop the second algorithm within a consistent hierarchical framework. Preliminary experimental results have demonstrated the superior performance of the proposed approaches on real-world videos in both robustness and speed compared with other articulated object tracking methods.

Porting well known computer vision algorithms to low power, high performance computing devices such as SIMD linear processor arrays can be a challenging task. One especially useful such algorithm is the color-based particle filter, which has been applied successfully by many research groups to the problem of tracking non-rigid objects. In this paper, we propose an implementation of the color-based particle filter suitable for SIMD processors. The main focus of our work is on the parallel computation of the particle weights. This step is the major bottleneck of standard implementations of the color-based particle filter since it requires the knowledge of the histograms of the regions surrounding each hypothesized target position. We expect this approach to perform faster in an SIMD processor than an implementation in a standard desktop computer even running at much lower clock speeds.

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e.g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter usin...

To reduce the damage from fire disaster, demand for automatic detection system by using computer vision technique is increasing. But because of false detections that are caused by various situations, it is hard to use in real. In fire detection area, to overcome this problem, the algorithm using several temporal and spatial information of object is proposed. Colour, brightness, and movement information is used to make relevant smoke detection algorithm. And continuous monitoring for several constraints is used to avoid false detections that are caused by unexpected behaviour of object. In experimental result, total 11 videos that have smoke and the other 2 videos that have no smoke is used for test performance of proposed method. It shows relevant performance for false detection and could detect almost fire in video.

Reliable object tracking at video speed is a task that requires high performance. We have analyzed the various parts of typical algorithms and found that some parts of program flows are best performed on a parallel platform for performance reasons and other parts are better performed on a ...

Computer Vision Winter Workshop 2006, Ondrej Chum, Vojtech Franc (eds.) Telc, Czech Republic, February 6–8 Czech Pattern Recognition Society ... Josep Aguilera1, David Thirde2, Martin Kampel1, Mark Borg2, Gustavo Fernandez3, and James Ferryman2

Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.

Multiple objects tracking finds its applications in many high level vision analysis like object behaviour interpretation and gait recognition. In this paper, a feature based method to track the multiple moving objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu moments features from the motion segmented object blob and establishing the association of objects in the successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the robustness of the proposed method. The proposed method is assessed quantitatively using the precision and recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit the efficacy of the proposed method.

Lip reading has wide spread application, e.g. audiovisual Automatic Speech Recognition (A V-ASR), silent speech interface and person identification. Lip segmentation is one of important step in lip reading, because it provides basic information to be processed in subsequent steps. Lip tracking is a process of locating lip to associate lip in consecutive video fr ames. Chan-Vese model is a region-based segmentation algorithm, which also can be used as tracking method. This algorithm can detect boundary of object which not defined by gradient, while classical active contour can't be applied. This method also can detect object by any initial curve in the image, not necessarily surround the obj ect. This paper investigates about lip segmentation and tracking based on Chan-Vese model, preceded by the color segmentation.

The goal of this article is to review the state-of-the-art tracking methods, classify them into different cate-gories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns ...

Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a challenging problem and it's an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, then the temporal differencing and the SIFT method and the last one is the mean shift method. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper. Our proposed method is implemented and evaluated using CAVIAR database.

The unsupervised selection and posterior recognition of visual landmarks is a highly valuable perceptual capability for a mobile robot. Recently, in [6], we propose a system that aims to achieve this capability by combining a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. The top-down feedback is based on two information sources: i) An estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, ii) A set of weights that, according to the results of previous recognitions, controls the influence of different segmentation algorithms in the recognition of each landmark. In this paper we explore the benefits of extending our previous work by including a visual tracking step for each of the selected landmarks. Our intuition is that the inclusion o...