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Open AccessProceedings ArticleDOI

Challenge of multi-camera tracking

TLDR
In this paper, the distributed architectures of multi-camera tracking system based on camera processor and based on object agent have been compared and show that improving the computation ability of cameras and reducing the functions of control center is the key to solve the architecture challenges.
Abstract
Multi-camera tracking is quite different from single camera tracking in mathematical principles and application scenarios, and it faces new technology and system architecture challenges. The existing theories and algorithms used in object matching, cameras calibration and topology estimation, and information fusion have been reviewed and show that the integrated application of multi techniques and multi theories is the key to solve the technology challenges. The distributed architectures of multi-camera tracking system based on camera processor and based on object agent have been compared and show that improving the computation ability of cameras and reducing the functions of control center is the key to solve the architecture challenges.

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Citations
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Journal ArticleDOI

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

TL;DR: This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer.
Journal ArticleDOI

Literature Survey on Multi-Camera System and Its Application

TL;DR: This paper surveys the available literature in terms of multi-camera systems’ physical arrangements, calibrations, algorithms, and their advantages and disadvantages, which are surveillance, sports, education, and mobile phones.
Proceedings ArticleDOI

Orientation and scale invariant binary descriptor based on Haar wavelet

TL;DR: Extensive experimental results show that the proposed orientation and scale invariant binary descriptor significantly outperforms other five state-of-the-art binary descriptors in key-point matching systems.
Proceedings ArticleDOI

Descriptor Extraction and Distance Metric Learning for a Robust Person Re-Identification System

TL;DR: The results that were acquired from the VIPeR, CUHK01, and CUHK02 datasets show that the system is comparable to the chosen baseline system with differences of only about 1-4% differences while the system outperforms the baseline on the Market-1501 dataset.
Patent

Moving object tracking apparatus, moving object tracking method, and computer-readable medium

TL;DR: In this article, a moving object tracking apparatus (20, 20-2) includes an acquiring unit (211), an associating unit (212, 212-2), and an output control unit (213, 213-2).
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Book ChapterDOI

SURF: speeded up robust features

TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI

Speeded-Up Robust Features (SURF)

TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI

ORB: An efficient alternative to SIFT or SURF

TL;DR: This paper proposes a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise, and demonstrates through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations.
Proceedings ArticleDOI

The unscented Kalman filter for nonlinear estimation

TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
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What are the challenges in using multi-camera triangulation?

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