A survey on 2d object tracking in digital video

This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different techniques are given for different types of constraints in video

Facial feature localization using highly flexible yet sufficiently strict shape models

textAccurate and efficient localization of facial features is a crucial first step in many face-related computer vision tasks. Some of these tasks include, but not limited to: identity recognition, expression recognition, and head-pose estimation. Most effort in the field has been exerted towards developing better ways of modeling prior appearance knowledge and image observations. Modeling prior shape knowledge, on the other hand, has not been explored as much. In this dissertation I primarily focus on the limitations of the existing methods in terms of modeling the prior shape knowledge. I first introduce a new pose-constrained shape model. I describe my shape model as being "highly flexible yet sufficiently strict". Existing pose-constrained shape models are either too strict, and have questionable generalization power, or they are too loose, and have questionable localization accuracies. My model tries to find a good middle-ground by learning which shape constraints are more "informative" and should be kept, and which ones are not-so-important and may be omitted. I build my pose-constrained facial feature localization approach on this new shape model using a probabilistic graphical model framework. Within this framework, observed and unobserved variables are defined as the local image observations, and the feature locations, respectively. Feature localization, or "probabilistic inference", is then achieved by nonparametric belief propagation. I show that this approach outperforms other popular pose-constrained methods through qualitative and quantitative experiments. Next, I expand my pose-constrained localization approach to unconstrained setting using a multi-model strategy. While doing so, once again I identify and address the two key limitations of existing multi-model methods: 1) semantically and manually defining the models or "guiding" their generation, and 2) not having efficient and effective model selection strategies. First, I introduce an approach based on unsupervised clustering where the models are automatically learned from training data. Then, I complement this approach with an efficient and effective model selection strategy, which is based on a multi-class naive Bayesian classifier. This way, my method can have many more models, each with a higher level of expressive power, and consequently, provides a more effective partitioning of the face image space. This approach is validated through extensive experiments and comparisons with state-of-the-art methods on state-of-the-art datasets. In the last part of this dissertation I discuss a particular application of the previously introduced techniques; facial feature localization in unconstrained videos. I improve the frame-by-frame localization results, by estimating the actual head-movement from a sequence of noisy head-pose estimates, and then using this information for detecting and fixing the localization failures.Electrical and Computer Engineerin

Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking

3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectors’ state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results