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 mis-detections 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 track-let 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.

Reliably measuring the similarity of two shapes or images (instances) is an important problem for various computer vision applications such as classification, recognition, and retrieval. While pairwise measures take advantage of the geometric differences between two instances to quantify their similarity, recent advances use relationships among the population of instances when quantifying pairwise measures. In this paper, we propose a novel method which refines pairwise similarity measures using population cues by examining the most similar instances shared by the compared shapes or images. We then use this refined measure to organize instances into disjoint components that consist of similar instances. Connectivity is then established between components to avoid hard constraints on what instances can be retrieved, improving retrieval performance. To evaluate the proposed method we conduct experiments on the well-known MPEG-7 and Swedish Leaf shape datasets as well as the Nister and Stewenius image dataset. We show that the proposed method is versatile, performing very well on its own or in concert with existing methods.

In this paper, we propose two new, perceptually motivated strategies to better measure the similarity of 2D shape instances that are in the form of closed contours. The first strategy handles shapes that can be decomposed into a base structure and a set of inward or outward pointing “strand” structures, where a strand structure represents a very thin, elongated shape part attached to the base structure. The similarity of two such shape contours can be better described by measuring the similarity of their base structures and strand structures in different ways. The second strategy handles shapes that exhibit good bilateral symmetry. In many cases, such shapes are invariant to a certain level of scaling transformation along their symmetry axis. In our experiments, we show that these two strategies can be integrated into available shape matching methods to improve the performance of shape classification on several widely-used shape data sets.

Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular, we reorganize the input population into a tree structure that incorporates global information about the population of shape instances, where each node in the tree represents a shape instance and each edge connects two very similar shape instances. Using this organized tree, neighboring shape instances can be corresponded efficiently and accurately by a pair-wise method. In the experiments, we evaluate the proposed method and compare its performance to five available shape correspondence methods and show the proposed method achieves the accuracy of a global method with speed of a pair-wise method.