团队负责人姜晓燕老师的论文“Data Fusion-Based Multi-Object Tracking for Unconstrained Visual Sensor Networks” 被SCI期刊IEEE Access接收，祝贺！

Abstract:
Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the real-world PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.

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Keywords: Data fusion, graph theory, Internet of Things, particle filters, visual sensor networks.

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Abstract:
Analyzing large amounts of video data is one of the key challenges in the trend towards big data. In the field of medical research, for example, to analyze infected cardiac movements, stereo X-ray sequences of beating animal hearts implanted with radiopaque markers are recorded. As manual annotation of exact marker positions in large-scale recordings is time-consuming and infeasible, research on automatic tracking of multiple markers is a crucial task. We propose an efficient two-stage graph-based data association approach to tackle this problem. Difficulties of the sequences like 2D occlusions, low contrast, inhomogeneous movement, and inaccurate detections, are considered in the framework. Reconstructed 3D observations are modeled and connected using a weighted directed acyclic graph to obtain tracklets with high confidence via shortest path extraction. Afterwards, tracklets are linked into longer tracks by a tracklet graph in a similar manner while global features are adopted. The approach is validated on eight X-ray cardiac datasets of beating sheep hearts with various diseases. Outperforming standard tracking approaches, e.g. particle filter, the experimental results show a high accuracy comparable to human experts and efficiency in the meantime. The proposed approach is generic and can be directly applied to other video data as well.

团队负责人姜晓燕老师的论文“Multi-marker tracking for large-scale X-ray stereo video data”被SCI期刊《Signal Processing-Image Communication》 接收，祝贺！

Download: [官方链接]

Keywords: Multi-object tracking, Data association, Directed acyclic graph, X-ray, Big data.

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