Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

Planar Object Tracking in the Wild: A Benchmark

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201

A minimalistic approach to appearance-based visual SLAM

This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses

Open-world Person Re-Identification by Multi-Label Assignment Inference.

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

Learning Correspondence Structures for Person Re-identification

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.Comment: IEEE Trans. Image Processing, vol. 26, no. 5, pp. 2438-2453, 2017. The project page for this paper is available at http://min.sjtu.edu.cn/lwydemo/personReID.htm arXiv admin note: text overlap with arXiv:1504.0624

Person re-identification via efficient inference in fully connected CRF

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure