Convergence analysis of active contours

Parametric active contours, also called snakes, are a particular case of deformable models embebed in the image plane. The mathematical principles under their formulation represent the confluence of Geometry, Physics and Approximation Theory. Since their debut in 1978, their usefulness has been proven in medical image analysis, image segmentation, object tracking in video sequences, etc.

Moving object tracking is a tricky job in computer vision problems. In this approach, the object tracking system relies on the deterministic search of target, whose color content matches a reference histogram model. A simple RGB histogram-based color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by shape information. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this approach we combine between particle filter and the probability product kernels as a similarity measure using integral image to compute the histograms of all possible target regions of object tracking in video sequence. The shape similarity between a target and estimated regions in the video sequence is measured by their normalized histogram. Target of object tracking is created instantly by selecting an object from the video sequence by a rectangle. Experimental results have been presented to show the effectiveness of our proposed system.

This paper presents efficient object tracking in video sequences using multiple features by embedding mean shift into particle filters. When clutter background and occlusions are present. Particle filtering is used because it is very robust and performs well for non-linear and non-Gaussian dynamic state estimation problems. The image features, such as shape, texture, color, contours, and random motion appearance can be used to track the moving object(s) in videos. We proposed a smart video surveillance system with real-time as well as offline stored video database for moving object detection, classification and tracking capabilities. The graphical user interface of the proposed system using MATLAB 2012b is implemented that operates on both color and gray scale video images from a stationary camera. In this method, we proposed real time moving object detection and tracking system using static webcam that can processes 800*600, 640*480, and 320*240 resolution video sequences for capturing live scene as well as stored database of segmented videos. Issues related with object detection and tracking is jointly employed using mean shift and particle filters. Tracker will estimate the dynamic shape and random appearance of objects. Tracking requires location and shape of object in every segmented frame. Rectangular bounding box is used to improve the estimate of its shape and appearance. Target object may experience partial occlusions, intersection with other objects with similar color distributions, abrupt motion speed changes, and cluttered background. Test result shows improvement in terms of robustness, tracking drifts, accuracy of tracked bounding box to occlusions.

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.

Moving object tracking in video sequences is an important task in the field of computer vision. In this paper, we propose a new population-based algorithm namely simplified swarm optimization (SSO) for tracking arbitrary objects. In SSO, the object model is first projected into a high-dimensional feature space, then the particles will fly over image pixels to find an optimal match of the target. While searching for the optimum, SSO progressively analyzes the occlusion situation. If any occlusion or disappearance of the target object is detected, the movement rules for the searching particles will be adaptively adjusted to recapture the target object. Experimental results showed that the SSO can robustly track an arbitrary target in various challenging conditions. Furthermore, SSO is capable to have 40% faster in speed and 36% higher in accuracy rate than the traditional PSO for varied environment.

Particle filter (PF) is a method dedicated to posterior density estimations using weighted samples whose elements are called particles. In particular, this approach can be applied to object tracking in video sequences in complex situations and, in this paper, we focus on articulated object tracking, i.e., objects that can be decomposed as a set of subparts. One of PF's crucial step is a resampling step in which particles are resampled to avoid degeneracy problems. In this paper, we propose to exploit mathematical properties of articulated objects to swap conditionally independent subparts of the particles in order to generate new particle sets. We then introduce a new resampling method called Combinatorial Resampling that resamples over the particle set resulting from all the "admissible" swappings, the so-called combinatorial set. In essence, combinatorial resampling (CR) is quite similar to the combination of a crossover operator and a usual resampling, but there exists a fundamental difference between CR and the use of crossover operators: we prove that CR is sound, i.e., in a Bayesian framework, it is guaranteed to represent without any bias the posterior densities of the states over time. By construction, the particle sets produced by CR better represent the density to estimate over the whole state space than the original set and, therefore, CR produces higher quality samples. Unfortunately, the combinatorial set is generally of an exponential size and, therefore, to be scalable, we show how it can be implicitly constructed and resampled from, thus resulting in both an efficient and effective resampling scheme. Finally, through experimentations both on challenging synthetic and real video sequences, we also show that our resampling method outperforms all classical resampling methods both in terms of the quality of its results and in terms of computation times.

In this research, a novel approach for processing of single images and image sequences by using the Hierarchical Adaptive Karhunen-Loeve (KL)-based Transform (HA-KLT) is presented This approach is also suitable for block image coding, and for intra-frame processing of groups of correlated images. The main objective of the new transform is to achieve a high decorrelation of the image blocks (resp., images in the processed groups) as a result of the multi-level processing through adaptive KL transform of the sub-blocks (resp., sub-groups), on which the corresponding blocks (groups) are divided. After the execution of each hierarchical transform level, the sub-blocks (sub-groups) are rearranged in correspondence to their mutual correlation, starting from these, with maximum correlation. The kernel of the multi-level transform is the Adaptive KL Transform (AKLT), whose transform matrix could be a size 2 × 2 or 3 × 3. In the work, the algebraic method is presented, which calculates the elements of the AKLT matrix of size 2 × 2 and 3 × 3. Here the algorithms for 2D HA-KLT and 3D HA-KLT are also given in detail for the blocks of a single image and for the inter-frame processing of groups of images. The evaluations of the computational complexity of these algorithms are obtained, compared to that of the famous KLT. The basic advantages of the new approach for image processing through HA-KLT are a lower computational complexity and the parallel recursive calculations with small number of hierarchical levels, which opens the ability for real-time coding of video sequences. The HA-KLT algorithms are a basis for the creation of new efficient methods for compression of groups of correlated images and single images without visual loss, compression with motion compensation for TV and multi-view images, compression of medical and multispectral images, image fusion, face recognition in the reduced features’ space, object tracking in video sequences, among others.

Two challenge problems in tracking objects in video are object segmentation and matching. Most previous methods can work with some user-specific control. In this paper, we will propose an adaptive algorithm for colour object tracking in video sequences based on background subtraction and image matching by using multiresolution critical point filter (CPFs). Which the background subtraction segment the blob and identify all the objects in the image no matter what they are moving or not. Using results from segmentation phase, object tracking is performed using the multiresolution critical point filter algorithm. This is based on research from prof. Shinagawa's image interpolation. For now, our approach is suitable for tracking of the object through a sequence of 64*64 images.

Aiming at improving the performance of non-rigid object tracking in video sequences acquired by a stationary camera, an effective method based on the adaptive color segmentation and object part model was presented. In this work, we modeled background and obtained the foreground blobs with an effective adaptive background updating method based on Gaussian mixture model (GMM), and then the regions in part model were generated online by the color segmentation based on region-growth. The region features and constraints between regions were taken into account and used to perform object tracking effectively and flexibly even under object partial occlusion and deformation. Experimental results with different real-world scenarios demonstrate validity and robust of our solution.

One factor of human sperm health is sperm motility. Motility is the ability of sperm to move. Sperm with healthy motility move forward promptly, not inactive and not moving in circles. In this paper, we would like to analyse sperm motility by considering the problem of multi object tracking in video sequences of human sperms. The challenges in multi-sperm tracking are many human sperms have fast and unpredictable movement In addition, the sperm have similar size and shape comparing by each others. To solve this problem, we used sperm detection in each video sequence to get the position of sperms. In the same time, the estimated sperm position is calculated based on previous tracking by using Kalman Filter. Finally the positions of detected sperms are compared to estimation results by using Hungarian assignment method. In this way, the trajectory of each sperm can be conclude. This paper analyze sperm motility qualitatively based on the resulted sperm trajectories. The experiment results were conducted on both open video data and our own low-frame-rate video data. The experiment results shows that the proposed method can handle the challenges in multi sperm tracking, create their trajectory and then analyze their behaviors.

In this work we study the methods for pedestrian tracking in video sequences and indicate various applications of these methods ranging from surveillance systems to aiding the visually impaired persons. First, we define the general problem of object tracking that comprises the tasks of object detection, identifying the flow of object location in consecutive video images and finally analysis of the tracked trajectory data. We review the well known object tracking techniques i.e. the Mean-Shift and the CAMSHIFT algorithm and discuss their properties. Then we introduce the computational technique known as particle filtering (PF) and explain how we have applied it to the tasks of pedestrian tracking. We compare the PF approach against the Mean-Shift and the CAMSHIFT algorithms in terms of tracking robustness and the required computational demand. We conclude, that on the tested video sequences, the PF tracker outperforms the Mean-Shift and by a small margin the CAMSHIFT algorithm. The PF tracker requires more computational power, however, its tracking performance can be flexibly adjusted to the application requirements.

Moving object detection and tracking in video sequences is a task that emerges in various research fields of video processing, including video analysis and understanding, object-based coding and many related applications, such as content-based retrieval, remote surveillance and object recognition. This work revisits one of the most popular deformable templates for shape modeling and object tracking, the Snakes, and proposes a modified snake model and a probabilistic utilization of it for object tracking. Special attention has been drawn to complex natural (indoor and outdoor) sequences, where temporal clutter, abrupt motion and external lighting changes are crucial for the accuracy of the results, also focusing on the ability of the proposed approach to handle specific HCI problems, such as face and facial feature tracking. A variety of image sequences are used to illustrate the method's capability, providing theoretical explanation as well as experimental verification in specific tracking problems.

Active contours, or snakes, are widely applied on biomedical image segmentation. They are curves defined within an image domain that can move to object boundaries under the influence of internal forces and external forces, in which the internal forces are generally computed from curves themselves and external forces from image data. Designing external forces properly is a key point with active contour algorithms since the external forces play a leading role in the evolution of active contours. One of most popular external forces for active contour models is gradient vector flow (GVF). However, GVF is sensitive to noise and false edges, which limits its application area. To handle this problem, in this paper, we propose using GVF as reference to train a convolutional neural network to derive an external force. The derived external force is then integrated into the active contour models for curve evolution. Three clinical applications, segmentation of optic disk in fundus images, fluid in retinal optical coherence tomography images and fetal head in ultrasound images, are employed to evaluate the proposed method. The results show that the proposed method is very promising since it achieves competitive performance for different tasks compared to the state-of-the-art algorithms.

One of the most important problems in Computer Vision is the computation of the 2D projective transformation (homography) that maps features of planar objects in different images and videos. This computation is required by many applications such as image mosaicking, image registration, and augmented reality. The real-time performance imposes constraints on the methods used. In this paper, we address the real-time detection and tracking of planar objects in a video sequence where the object of interest is given by a reference image template. Most existing approaches for homography estimation are based on two steps: feature extraction (first step) followed by a combinatorial optimization method (second step) to match features between the reference template and the scene frame. This paper has two main contributions. First, for the detection part, we propose a feature point classification which is applied prior to performing the matching step in the process of homography calculation. Second, for the tracking part, we propose a fast method for the computation of the homography that is based on the transferred object features and their associated local rawbrightness. The advantage of this proposed scheme is a fast and accurate estimation of the homography.

In video processing, face detection and tracking has a wide scope for research. Although existing algorithms serve the purpose of face detection and tracking in video sequences, tremendous development in video technologies is posing more challenges while processing such videos. In this paper, we propose a new face tracking method based on fusion of corner measure algorithm and KLT tracker. Initially, Viola-Jones algorithm detect the face present in the first frame of the video sequence, detected portion of the face is extracted and Harris corner measure algorithm applied. Once corner points are computed, centroid is calculated. The resulting corner points which are in the form of an array are converted into matrix. KLT is a point tracking algorithm, works based on Eigen values and its result is in the form of matrix. The matrix generated from corner measure and centroid computation is concatenated with the matrix obtained using KLT face tracking algorithm. If the corner measure algorithm fails to track the facial region then KLT tracker performs its work and the reverse is also true. The proposed algorithm achieves accuracy better than KLT algorithm alone. The results at the end of this paper clearly indicate the practical achievement of theoretical assumption made.