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

FULL 3D RECONSTRUCTION OF DYNAMIC NON-RIGID SCENES: ACQUISITION AND ENHANCEMENT

Recent advances in commodity depth or 3D sensing technologies have enabled us to move closer to the goal of accurately sensing and modeling the 3D representations of complex dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and e-health, there is now a greater demand for aff ordable and flexible vision systems which are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems. This thesis targets these limitations and builds approaches around commodity 3D sensing technologies to acquire noise-free and feature preserving full 3D reconstructions of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system composed of several RGB-D cameras covering the whole scene is used. In the first part of this thesis, the domain of correctly aligning the information acquired from RGB-D cameras in a multi-view system to provide full and textured 3D reconstructions of dynamic scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration problem. This thesis proposes an extrinsic calibration framework which uses the 2D photometric and 3D geometric information, acquired with RGB-D cameras, according to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic calibration problem simultaneously. Results show improvement in calibration accuracy as compared to state-of-art methods. In the second part of this thesis, the domain of enhancement of noisy and low-resolution 3D data acquired with commodity RGB-D cameras in both mono-view and multi-view systems is explored. This thesis extends the state-of-art in mono-view template-free recursive 3D data enhancement which targets dynamic scenes containing rigid-objects, and thus requires tracking only the global motions of those objects for view-dependent surface representation and fi ltering. This thesis proposes to target dynamic scenes containing non-rigid objects which introduces the complex requirements of tracking relatively large local motions and maintaining data organization for view-dependent surface representation. The proposed method is shown to be e ffective in handling non-rigid objects of changing topologies. Building upon the previous work, this thesis overcomes the requirement of data organization by proposing an approach based on view-independent surface representation. View-independence decreases the complexity of the proposed algorithm and allows it the flexibility to process and enhance noisy data, acquired with multiple cameras in a multi-view system, simultaneously. Moreover, qualitative and quantitative experimental analysis shows this method to be more accurate in removing noise to produce enhanced 3D reconstructions of non-rigid objects. Although, extending this method to a multi-view system would allow for obtaining instantaneous enhanced full 360 degrees 3D reconstructions of non-rigid objects, it still lacks the ability to explicitly handle low-resolution data. Therefore, this thesis proposes a novel recursive dynamic multi-frame 3D super-resolution algorithm together with a novel 3D bilateral total variation regularization to filter out the noise, recover details and enhance the resolution of data acquired from commodity cameras in a multi-view system. Results show that this method is able to build accurate, smooth and feature preserving full 360 degrees 3D reconstructions of the dynamic scenes containing non-rigid objects

Temporally coherent 4D reconstruction of complex dynamic scenes

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 . Video available at: https://www.youtube.com/watch?v=bm_P13_-Ds

Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations

We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low spatial resolutions. While solutions for reconstructing 3D details in static scenes, or scenes with rigid global motions have been recently proposed, handling unconstrained non-rigid deformations in relative complex scenes remains a challenge. Our solution consists in a recursive dynamic multi-frame superresolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per-pixel tracking where both depth measurements and deformations are dynamically optimized. The geometric smoothness is subsequently added using a multi-level L1 minimization with a bilateral total variation regularization. The performance of this method is thoroughly evaluated on both real and synthetic data. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

Better Feature Tracking Through Subspace Constraints

Feature tracking in video is a crucial task in computer vision. Usually, the tracking problem is handled one feature at a time, using a single-feature tracker like the Kanade-Lucas-Tomasi algorithm, or one of its derivatives. While this approach works quite well when dealing with high-quality video and "strong" features, it often falters when faced with dark and noisy video containing low-quality features. We present a framework for jointly tracking a set of features, which enables sharing information between the different features in the scene. We show that our method can be employed to track features for both rigid and nonrigid motions (possibly of few moving bodies) even when some features are occluded. Furthermore, it can be used to significantly improve tracking results in poorly-lit scenes (where there is a mix of good and bad features). Our approach does not require direct modeling of the structure or the motion of the scene, and runs in real time on a single CPU core.Comment: 8 pages, 2 figures. CVPR 201