An Equalized Global Graph Model-Based Approach for Multicamera Object Tracking

Chen, Weihua; Cao, Lijun; Chen, Xiaotang; Huang, Kaiqi

doi:10.1109/tcsvt.2016.2589619

Cited by 79 publications

(53 citation statements)

References 62 publications

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“…An attributed graph, as its name implies, defines a set of values (attributes) for each vertex in a graph to represent the features extracted from the input video/image frame. The graph representation is utilized in combination with a particle filter [4], feature extraction using DCT coefficients [5], utilizing geometric transformation [6], learning-based structured graph matching [7], global-modeling graph with similarity metric for non-overlapping multi-camera object tracking [8], or utilizing non-RGB information in graph-based object tracking [9].…”

Section: Related Workmentioning

confidence: 99%

Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View

Sabirin

Nishimura

Naito

2019

IEICE Trans. Inf. & Syst.

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A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.

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Section: Related Workmentioning

confidence: 99%

Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View

Sabirin

Nishimura

Naito

2019

IEICE Trans. Inf. & Syst.

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“…In addition, to better achieve tracklet matching across multiple camera views, the minimum uncertainty gap-based measurement, i.e., using the lowest and highest similarity to define the lower and upper bounds of the similarity for two tracklets to obtain a distance metric, is applied to computing the matching result of two tracklets' PMCSHRs. Built upon the research of PMCSHR [62], Chen et al [63] equalize similarity metrics in the global graph based on appearance and motion features, and hence further reduce the number of mismatch errors in non-overlapping inter-camera human tracking so as to further improve human tracking performance across non-overlapping cameras. Table 6 lists several quantitative comparison results of GM-based tracking across non-overlapping cameras on NLPR datasets, using multiple camera tracking accuracy (MCTA) to evaluate the performance of GM-based tracking, where the higher the MCTA is, the better the performance of GM-based tracking.…”

Section: Gm-based Trackingmentioning

confidence: 99%

Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across nonoverlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

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“…Milan et al constructed a carefully annotated dataset for Multi-Object Tracking Challenge 2016 [ 10 ]. In addition, the datasets which provided non-overlapping multi-camera videos covering the same objects were adopted in this paper provided by Chen et al [ 11 ] and Ristani et al [ 12 ]. No matter which dataset is adopted, there are still some common challenges: frequent occlusion among crowds, accurate determination of the beginning and end of a trajectory, similar appearance among pedestrians, and missing details due to low resolution.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in order to handle the problem that the network can hardly converge effectively on large and complex datasets, a sigmoid normalize operation is applied. For multi-object tracking across cameras, we design two strategies: a simple sequence-to-sequence matching algorithm and tracking algorithm by embedding the feature extraction in EGTracker in [ 11 ]. The experiments have demonstrated that the proposed method can extract efficient features and improve the performance of tracking systems when applying the learned features.…”

Section: Introductionmentioning

confidence: 99%

NCA-Net for Tracking Multiple Objects across Multiple Cameras

Tan

Tai

Xiong

2018

Sensors

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Tracking multiple pedestrians across multi-camera scenarios is an important part of intelligent video surveillance and has great potential application for public security, which has been an attractive topic in the literature in recent years. In most previous methods, artificial features such as color histograms, HOG descriptors and Haar-like feature were adopted to associate objects among different cameras. But there are still many challenges caused by low resolution, variation of illumination, complex background and posture change. In this paper, a feature extraction network named NCA-Net is designed to improve the performance of multiple objects tracking across multiple cameras by avoiding the problem of insufficient robustness caused by hand-crafted features. The network combines features learning and metric learning via a Convolutional Neural Network (CNN) model and the loss function similar to neighborhood components analysis (NCA). The loss function is adapted from the probability loss of NCA aiming at object tracking. The experiments conducted on the NLPR_MCT dataset show that we obtain satisfactory results even with a simple matching operation. In addition, we embed the proposed NCA-Net with two existing tracking systems. The experimental results on the corresponding datasets demonstrate that the extracted features using NCA-net can effectively make improvement on the tracking performance.

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An Equalized Global Graph Model-Based Approach for Multicamera Object Tracking

Cited by 79 publications

References 62 publications

Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View

Synchronized Tracking in Multiple Omnidirectional Cameras with Overlapping View

Human tracking over camera networks: a review

NCA-Net for Tracking Multiple Objects across Multiple Cameras

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