A novel fast object tracking method based on hierarchical block matching

Block matching based motion estimation algorithms are adopted in numerous practical video processing applications due to their low complexity. However, conventional block matching based methods process each block independently to minimize the energy function, which results in a local minimum. It fails to preserve the motion details. In this paper, we formulate the motion estimation as a labeling problem. The candidate labels are initialized by adopting a hierarchical block matching method. Then, we employ a graph cut algorithm to efficiently solve the global labeling problem with candidate labels. Experimental results show that the proposed approach can well preserve the motion details and outperforms all other block based motion estimation methods in terms of endpoint error and angle error on the Middleburry optical flow benchmark.

This paper proposes a 2D face recognition algorithm using phase-based correspondence matching. The phase information obtained from 2D DFT (Discrete Fourier Transform) of images contains important information of image representation. The phase-based image matching is successfully applied to sub-pixel image registration tasks for computer vision applications and image recognition tasks for biometric authentication applications. Hierarchical block matching using phase information, i.e, phase-based correspondence matching, can find the corresponding points on the input image from the reference points on the registered image with sub-pixel accuracy. For face recognition, the phase-based correspondence matching is useful for minute change of texture, such as facial expression change, illumination change, etc. Experimental evaluation using the CSU Face Identification Evaluation System with the FERET database demonstrates efficient recognition performance of the proposed algorithm compared with the conventional face recognition algorithms.

Video object segmentation for object based video coding according to MPEG-4 should be able to combine many features of the image sequence to achieve high quality object segmentation. The significant features include color, motion vector and change information. This paper presents an object segmentation algorithm where the image features are combined for use in the segmentation process. A fast recursive shortest spanning tree (FRSST) algorithm is used to segment the image into different intensity regions because of the good image segmentation performance and the fast algorithm. Motion vectors are estimated from consecutive frames and searched by thresholding hierarchical block matching (HBM) which require low computation and give acceptable results. After that many features are integrated in the segmentation decision process. Then, the postprocessing refines the final segmentation output. The results from some test sequences especially in a video conference sequence have a good performance and show the object of interest boundary clearly.

Video segmentation for object based video coding according to MPEG-4 should be able to segment interested objects in video sequence clearly. This paper presents the object segmentation algorithm which image features are combined to use in segmentation process following to characteristic of video signal. Because the combination of many features in video sequence is a method that can achieve high quality object segmentation. In addition, this algorithm is an adaptive method that many parameters can be adjusted in order to give clearly segmentation. The significant features are used in segmentation process including color, motion vector and change information. A fast shortest spanning tree (SST) algorithm is adapted to use for fast segmenting image boundary. Motion vectors are estimated and searched by thresholding hierarchical block matching (HBM) which want quite low computation and give a few groups of motion vectors. The change information is used to detect moving objects that can separate between moving object and static background. After that, each feature will be considered in segmentation decision process to segment interested objects. Then the post processing refines the final segmentation. The results from many test sequences have good quality and show object boundary clearly.

Detection and analysis of events from video sequences is probably one of the most important research issues in computer vision and pattern analysis society. Before, however, applying methods and tools for analyzing actions, behavior or events, we need to implement robust and reliable tracking algorithms able to automatically monitor the movements of many objects in the scene regardless of the complexity of the background, existence of occlusions and illumination changes. Despite the recent research efforts in the field of object tracking, the main limitation of most of the existing algorithms is that they are not enriched with automatic recovery strategies able to re-initialize tracking whenever its performance severely deteriorates. This is addressed in this paper by proposing an automatic tracking recovery tool which improves the performance of any tracking algorithm whenever the results are not acceptable. For the recovery, non-linear object modeling tools are used which probabilistically label image regions to object classes. The models are also time varying. The first property is implemented in our case using concepts from functional analysis which allow parametrization of any arbitrary non-linear function (with some restrictions on its continuity) as a finite series of known functional components but of unknown coefficients. The second property is addressed by proposing an innovative algorithm that optimally estimates the non-linear model at an upcoming time instance based on the current non-linear models that have been already approximated. The architecture is enhanced by a decision mechanism which permits verification of the time instances in which tracking recovery should take place. Experimental results on a set of different video sequences that present complex visual phenomena (full and partial occlusions, illumination variations, complex background, etc) are depicted to demonstrate the efficiency of the proposed scheme in proving tracking in very difficult visual content conditions. Additionally, criteria are proposed to objectively evaluate the tracking performance and compare it with other strategies.

The focus of this paper is on the problem of recognizing faces across space-varying motion blur, changes in pose, illumination, and expression, as well as partial occlusion, when only a single image per subject is available in the gallery. We show how the blur, incurred due to relative motion between the camera and the subject during exposure, can be estimated from the alpha matte of pixels that straddle the boundary between the face and the background. We also devise a strategy to automatically generate the trimap required for matte estimation. Having computed the motion via the matte of the probe, we account for pose variations by synthesizing from the intensity image of the frontal gallery a face image that matches the pose of the probe. To handle illumination, expression variations, and partial occlusion, we model the probe as a linear combination of nine blurred illumination basis images in the synthesized nonfrontal pose, plus a sparse occlusion. We also advocate a recognition metric that capitalizes on the sparsity of the occluded pixels. The performance of our method is extensively validated on synthetic as well as real face data.

Directing against the huge amounts of calculation for traditional target tracking, a fast target tracking algorithm based on image correlation matching is proposed in this paper. Firstly, for the continuity of movement target in the real-time image sequences, the image matching algorithm based on wavelet transform is used to match the first frame image to determine the best match point, then the best match point is used as initial point and combined with prediction characteristics of Kalman filter to predict the possible position of the target on the next frame and optimal matching point of the next frame. Eventually, it realized a fast and accurate target tracking algorithm. The experimental results show that the proposed matching algorithm can provide favorable tracking performance and improve the speed and accuracy of target tracking, which meets the real-time request of system.

We propose a fragment based algorithm for efficient target tracking under significant scale variation and partial occlusion. In contrast, none of the previous multiple part or fragment based algorithms are both scale adaptive and robust to partial occlusion. In our algorithm, the target is divided into a number of overlapping image fragments. Their color histograms are compared with those of candidate fragments within the neighborhood in subsequent frames. The candidate fragments with maximum histogram similarity with the template fragments contribute towards selection of location of best match within that neighborhood. Its position is further calibrated by using a localization method. We implement a systematic scale adaptive tracking scheme which is robust to significant changes in the target size. Extensive experimental results based on real life sequences and test datasets are presented which demonstrate the excellent tracking accuracy achieved by our algorithm at real time.

This paper describes an approach for lip tracking using dynamic deformable templates. The objective is to track lip parameters from an audio-visual corpus recording a voice talent who is reading text prompts in a natural and expressive way. The corpus presents challenges to the conventional method of lip tracking with deformable templates. This is because natural and expressive speech includes relatively large motions of the head and the lips. The head motions lead to changes in the illumination of the face region and changes in the observed lip shape. In addition, emphatic pronunciations lead to large changes in the lip shape. Video frames that are affected by face illumination changes present additional difficulty in locating the mouth region (i.e. region of interest, ROI). Video frames that are affected by changes in lip shapes present additional deviations from the lip templates and hence lower tracking accuracies. Our proposed method incorporates dynamicity in the deformable templates to render them adaptive to changes in head pose, face illumination and lip shapes. Experiments show that dynamic deformable templates consistently outperform the conventional deformable templates in lip tracking.

This paper describes an approach for lip tracking using dynamic deformable templates. The objective is to track lip parameters from an audio-visual corpus recording a voice talent who is reading text prompts in a natural and expressive way. The corpus presents challenges to the conventional method of lip tracking with deformable templates. This is because natural and expressive speech includes relatively large motions of the head and the lips. The head motions lead to changes in the illumination of the face region and changes in the observed lip shape. In addition, emphatic pronunciations lead to large changes in the lip shape. Video frames that are affected by face illumination changes present additional difficulty in locating the mouth region (i.e. region of interest, ROI). Video frames that are affected by changes in lip shapes present additional deviations from the lip templates and hence lower tracking accuracies. Our proposed method incorporates "dynamicity" in the deformable templates to render them adaptive to changes in head pose, face illumination and lip shapes. Experiments show that dynamic deformable templates consistently outperform the conventional deformable templates in lip tracking.

This paper addresses the issues of poor robustness and high computational complexity in traditional multi-target tracking algorithms. To tackle these challenges, the paper introduces the "anchoring" mechanism, which effectively resolves the problem of repeated detection of targets with low confidence in multi-target tracking tasks. Additionally, an "anchor function" is proposed to quantify the target coordinate features and anchor frame features, enabling accurate and computationally efficient multi-target matching for low-speed and stationary targets using a threshold-based approach. As a result, a fast detection-based target tracking (FDBT) algorithm is developed. Experimental results demonstrate that the FDBT algorithm achieves stable and high-precision tracking of satellite images, while significantly improving tracking efficiency compared to the DeepSort algorithm.

A fundamental issue in differential motion analysis is the compromise between the flexibility of the matching criterion for image regions and the ability of recovering the motion. Localized matching criteria, e.g., pixel-based SSD, may enable the recovery of all motion parameters, but it does not tolerate much appearance changes. On the other hand, global criteria, e.g., matching histograms, can accommodate dramatic appearance changes, but may be blind to some motion parameters, e.g., scaling and rotation. This paper presents a novel differential approach that integrates the advantages of both in a principled way based on a spatial-appearance model (SAM) that combines local appearances variations and global spatial structures. This model can capture a large variety of appearance variations that are attributed to the local non-rigidity. At the same time, this model enables efficient recovery of all motion parameters. A maximum likelihood matching criterion is defined and rigorous analytical results are obtained that lead to a closed form solution to motion tracking. Very encouraging results demonstrate the effectiveness and efficiency of the proposed method for tracking non-rigid objects that exhibit dramatic appearance deformations, large object scale changes and partial occlusions.

Robust visual tracking base on adaptively multi-feature fusion and particle filter

Heart rate measurement, especially out of clinical environments, has recently become a hot issue in the field of health monitoring. Video-based heart rate measurement is contactless and unobtrusive. However, most video-based heart rate measurements are sensitive to illumination changes. Inspired by a previous study which measures heart rate according to ballistocardiographic motion sensed by an ear-worn tri-axial accelerometer, we proposes to detect the ballistocardiographic motion of ear by tracking ear features in a video. Then we provide a video-based noncontact heart rate monitoring method. Scale-invariant features are employed to describe the structural characteristics of the ear, invariant to illumination changes and partial occlusion, which helps achieve movements of the ear. Relying on the signals of movements of ear features, underlying ballistocardiographic motion signal is separated and spectrum analyzed to estimate the heart rate. Experimental results demonstrate that by monitoring videos our method can obtain accurate heart rates close to the measurements gained by a contact sensor.

Robust motion estimation under varying illumination