The major challenge in constructing a statistical shape model for a structure is shape correspondence, which identifies a set of corresponded landmarks across a population of shape instances to accurately estimate the underlying shape variation. Both global or pairwise shapecorrespondence methods have been developed to automatically identify the corresponded landmarks. For global methods, landmarks are found by optimizing a comprehensive objective function that considers the entire population of shape instances. While global methods can produce very accurate shape correspondence, they tend to be very inefficient when the population size is large. For pairwise methods, all shape instances are corresponded to a given template independently. Therefore, pairwise methods are usually very efficient. However, if the population exhibits a large amount of shape variation, pairwise methods may produce very poor shape correspondence. In this paper, we develop a new method that attempts to address the limitations of global and pairwise methods. In particular, we first construct a shape tree to globally organize the population of shape instances by identifying similar shape instance pairs. We then perform pairwise shape correspondence between such similar shape instances with high accuracy. Finally, we combine these pairwise correspondences to achieve a unified correspondence for the entire population of shape instances. We evaluate the proposed method by comparing its performance to five available shape correspondence methods, and show that the proposed method achieves the accuracy of a global method with the efficiency of a pairwise method.
Multi-target tracking plays a key role in many computer vision applications including robotics, human-computer interaction, event recognition, etc., and has received increasing attention in past several years. Starting with an object detector is one of many approaches used by existing multi-target tracking methods to create initial short tracks called tracklets. These tracklets are then gradually grouped into longer final tracks in a heirarchical framework. Although object detectors have greatly improved in recent years, these detectors are far from perfect and can fail to detect the object of interest or identify a false positive as the desired object. Due to the presence of false positives or misdetections from the object detector, these tracking methods can suffer from track fragmentations and identity switches. To address this problem, we formulate multi-target tracking as a min-cost flow graph problem which we call the average shortest path. This average shortest path is designed to be less biased towards the track length. In our average shortest path framework, object misdetection is treated as an occlusion and is represented by the edges between tracklet nodes across non consecutive frames. We evaluate our method on the publicly available ETH dataset. Camera motion and long occlusions in a busy street scene make ETH a challenging dataset. We achieve competitive results with lower identity switches on this dataset as compared to the state of the art methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.