3 research outputs found
Recommended from our members
Accurate Joint Detection from Depth Videos towards Pose Analysis
Joint detection is vital for characterizing human pose and serves as a foundation for a wide range of computer vision applications such as physical training, health care, entertainment. This dissertation proposed two methods to detect joints in the human body for pose analysis. The first method detects joints by combining body model and automatic feature points detection together. The human body model maps the detected extreme points to the corresponding body parts of the model and detects the position of implicit joints. The dominant joints are detected after implicit joints and extreme points are located by a shortest path based methods. The main contribution of this work is a hybrid framework to detect joints on the human body to achieve robustness to different body shapes or proportions, pose variations and occlusions. Another contribution of this work is the idea of using geodesic features of the human body to build a model for guiding the human pose detection and estimation. The second proposed method detects joints by segmenting human body into parts first and then detect joints by making the detection algorithm focusing on each limb. The advantage of applying body part segmentation first is that the body segmentation method narrows down the searching area for each joint so that the joint detection method can provide more stable and accurate results
Generic multiple object tracking
Multiple object tracking is an important problem in the computer vision community due to its applications, including but not limited to, visual surveillance, crowd behavior analysis and robotics. The difficulties of this problem lie in several challenges such as frequent occlusion,
interaction, high-degree articulation, etc. In recent years, data association based approaches have been successful in tracking multiple pedestrians on top of specific kinds of object detectors. Thus these approaches are type-specific. This may constrain their application in scenario where type-specific object detectors are unavailable. In view of this, I investigate in this thesis tracking multiple objects without ready-to-use and type-specific object detectors. More specifically, the problem of multiple object tracking is generalized to tracking targets of a generic type. Namely, objects to be tracked are no longer constrained to be a specific kind of objects. This problem is termed as Generic Multiple Object Tracking (GMOT), which is handled by three approaches presented in this thesis. In the first approach, a generic object detector is learned based on manual annotation of only one initial bounding box. Then the detector is employed to regularize the online learning procedure of multiple trackers which are specialized to each object. More specifically, multiple trackers are learned simultaneously with shared features and are guided to keep close to the detector. Experimental results have shown considerable improvement on this problem compared with the state-of-the-art methods. The second approach treats detection and tracking of
multiple generic objects as a bi-label propagation procedure, which is consisted of class label
propagation (detection) and object label propagation (tracking). In particular, the cluster Multiple Task Learning (cMTL) is employed along with the spatio-temporal consistency to address
the online detection problem. The tracking problem is addressed by associating existing trajectories with new detection responses considering appearance, motion and context information. The advantages of this approach is verified by extensive experiments on several public data sets. The aforementioned two approaches handle GMOT in an online manner. In contrast, a batch method is proposed in the third work. It dynamically clusters given detection hypotheses into groups corresponding to individual objects. Inspired by the success of topic model in tackling textual tasks, Dirichlet Process Mixture Model (DPMM) is utilized to address the tracking problem by cooperating with the so-called must-links and cannot-links, which are proposed to avoid physical collision. Moreover, two kinds of representations, superpixel and Deformable Part Model (DPM), are introduced to track both rigid and non-rigid objects. Effectiveness of the proposed method is demonstrated with experiments on public data sets.Open Acces