Object Tracking with Adaptive Multicue Incremental Visual Tracker

Generally, subspace learning based methods such as the Incremental Visual Tracker (IVT) have been shown to be quite effective for visual tracking problem. However, it may fail to follow the target when it undergoes drastic pose or illumination changes. In this work, we present a novel tracker to enhance the IVT algorithm by employing a multicue based adaptive appearance model. First, we carry out the integration of cues both in feature space and in geometric space. Second, the integration directly depends on the dynamically-changing reliabilities of visual cues. These two aspects of our method allow the tracker to easily adapt itself to the changes in the context and accordingly improve the tracking accuracy by resolving the ambiguities. Experimental results demonstrate that subspace-based tracking is strongly improved by exploiting the multiple cues through the proposed algorithm

Multicue-based crowd segmentation using appearance and motion

In this paper, our aim is to segment a foreground region into individual persons in crowded scenes. We will focus on the combination of multiple clues for crowd segmentation. To ensure a wide range of applications, few assumptions are needed on the scenarios. In the developed method, crowd segmentation is formulated as a process to group the feature points with a human model. It is assumed that a foreground region has been detected and that an informative foreground contour is not required. The approach adopts a block-based implicit shape model (B-ISM) to collect some typical patches from a human being and assess the possibility of their occurrence in each part of a body. The combination of appearance cues with coherent motion of the feature points in each individual is considered. Some results based on the USC-Campus sequence and the CAVIAR data set have been shown. The contributions of this paper are threefold. First, a new B-ISM model is developed, and it is combined with joint occlusion analysis for crowd segmentation. The requirement for an accurate foreground contour is reduced. In addition, ambiguity in a dense area can be handled by collecting the evidences inside the crowd region based on the B-ISM. Furthermore, motion cues-which are coherent moving trajectories of feature points from individuals'are combined with appearance cues to help segment the foreground region into individuals. The usage of motion cues can be an effective supplement to appearance cues, particularly when the background is cluttered or the crowd is dense. Third, three features have been proposed to distinguish points on rigid body parts from those with articulated movements. Coherent motion of feature points on each individual can be more reliably identified by excluding points with articulated motion. © 2012 IEEE.published_or_final_versio

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

A Novel and Effective Short Track Speed Skating Tracking System

This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system

Performance evaluation for tracker-level fusion in video tracking

PhDTracker-level fusion for video tracking combines outputs (state estimations) from multiple trackers, to address the shortcomings of individual trackers. Furthermore, performance evaluation of trackers at run time (online) can determine low performing trackers that can be removed from the fusion. This thesis presents a tracker-level fusion framework that performs online tracking performance evaluation for fusion. We first introduce a method to determine time instants of tracker failure that is divided into two steps. First, we evaluate tracking performance by comparing the distributions of the tracker state and a region around the state. We use Distribution Fields to generate the distributions of both regions and compute a tracking performance score by comparing the distributions using the L1 distance. Then, we model this score as a time series and employ the Auto Regressive Moving Average method to forecast future values of the performance score. A difference between the original and forecast returns the forecast error signal that we use to detect tracking failure. We test the method with different datasets and then demonstrate its flexibility using tracking results and sequences from the Visual Object Tracking (VOT) challenge. The second part presents a tracker-level fusion method that combines the outputs of multiple trackers. The method is divided into three steps. First, we group trackers into clusters based on the spatio-temporal pair-wise relationships of their outputs. Then, we evaluate tracking performance based on reverse-time analysis with an adaptive reference frame and define the cluster with trackers that appear to be successfully following the target as the on-target cluster. Finally, we fuse the outputs of the trackers in the on-target cluster to obtain the final target state. The fusion approach uses standard tracker outputs and can therefore combine various types of trackers. We test the method with several combinations of state-of-the-art trackers, and also compare it with individual trackers and other fusion approaches.EACEA, under the EMJD ICE Project

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

3D Non-Rigid Reconstruction with Prior Shape Constraints

3D non-rigid shape recovery from a single uncalibrated camera is a challenging, under-constrained problem in computer vision. Although tremendous progress has been achieved towards solving the problem, two main limitations still exist in most previous solutions. First, current methods focus on non-incremental solutions, that is, the algorithms require collection of all the measurement data before the reconstruction takes place. This methodology is inherently unsuitable for applications requiring real-time solutions. At the same time, most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. Note that specific types of shape variation might be governed by only a small number of parameters and therefore can be well-represented in a low dimensional manifold. The methods proposed in this thesis aim to estimate the non-rigid shapes and the corresponding camera trajectories, based on both the observations and the prior learned manifold. Firstly, an incremental approach is proposed for estimating the deformable objects. An important advantage of this method is the ability to reconstruct the 3D shape from a newly observed image and update the parameters in 3D shape space. However, this recursive method assumes the deformable shapes only have small variations from a mean shape, thus is still not feasible for objects subject to large scale deformations. To address this problem, a series of approaches are proposed, all based on non-linear manifold learning techniques. Such manifold is used as a shape prior, with the reconstructed shapes constrained to lie within the manifold. Those non-linear manifold based approaches significantly improve the quality of reconstructed results and are well-adapted to different types of shapes undergoing significant and complex deformations. Throughout the thesis, methods are validated quantitatively on 2D points sequences projected from the 3D motion capture data for a ground truth comparison, and are qualitatively demonstrated on real example of 2D video sequences. Comparisons are made for the proposed methods against several state-of-the-art techniques, with results shown for a variety of challenging deformable objects. Extensive experiments also demonstrate the robustness of the proposed algorithms with respect to measurement noise and missing data