An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World Environments

Griffiths, Erin; Kalyanaraman, Avinash; Whitehouse, Kamin

doi:10.1145/3089157

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Visual Object Tracking (VOT) aims at detecting the position and scale of an object of interest in every frame of a video. The tracking capability plays a significant role in the gamut of perceptual functionalities in computer vision and pattern recognition (Xue et al 2020;Griffiths et al 2017;Smeulders et al 2013). The development of visual object tracking techniques has been ongoing for decades.…”

Section: Introductionmentioning

confidence: 99%

RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking

Zhu

Tang

et al. 2023

AAAI

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RGB-D object tracking has attracted considerable attention recently, achieving promising performance thanks to the symbiosis between visual and depth channels. However, given a limited amount of annotated RGB-D tracking data, most state-of-the-art RGB-D trackers are simple extensions of high-performance RGB-only trackers, without fully exploiting the underlying potential of the depth channel in the offline training stage. To address the dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this paper. The RGBD1K contains 1,050 sequences with about 2.5M frames in total. To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset. The results, of extensive experiments using the SPT tracker demonstrate the potential of the RGBD1K dataset to improve the performance of RGB-D tracking, inspiring future developments of effective tracker designs. The dataset and codes will be available on the project homepage: https://github.com/xuefeng-zhu5/RGBD1K.

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Section: Introductionmentioning

confidence: 99%

RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking

Zhu

Tang

et al. 2023

AAAI

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“…V Isual object tracking, as a fundamental research topic in computer vision and pattern recognition, aims to automatically predict the state of an object of interest in each frame of a video. It plays a significant role in various practical applications, including automatic driving, humancomputer interaction, intelligent surveillance, and so on [1], [2], [3]. Over the past decades, visual object tracking has achieved significant progress [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking on Multi-modal RGB-D Videos: A Review

Zhu¹,

Xu²,

Wu³

2022

Preprint

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The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB-D videos has drawn much attention. Compared to conventional RGB-only tracking, the RGB-D videos can provide more information that facilitates objecting tracking in some complicated scenarios. The goal of this review is to summarize the relative knowledge of the research filed of RGB-D tracking. To be specific, we will generalize the related RGB-D tracking benchmarking datasets as well as the corresponding performance measurements. Besides, the existing RGB-D tracking methods are summarized in the paper. Moreover, we discuss the possible future direction in the field of RGB-D tracking.

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“…A doorway counter is the most obvious type of device for counting the number of people in a certain room or part of a building. [18] presents a review of available technologies for such a counter. A doorway counter has 96% accuracy in indicating whether a room is occupied or not, 91 % accuracy in showing the time a room has been occupied and 87% accuracy in showing times a person has entered in or exited from a room.…”

Section: Introductionmentioning

confidence: 99%

A Low-Resolution IR-Array as a Doorway Occupancy Counter in a Smart Building

Maaspuro

2020

Int. J. Onl. Eng.

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A doorway counter, which detects a person underpass at a room entry/exit, may be the most accurate type of occupancy counters used in buildings. An occupancy counter, which uses a low-resolution IR-imager and Raspberry Pi board has been constructed. The imager provides only 8 x 8 pixels initial resolution, but it has been enhanced using two-dimensional interpolation. Due to the low absolute accuracy in temperature measurements, the imager is set to measure temperature difference between a target and background. Signal-to-noise ratio is also increased using discrete two-dimensional convolution filtering. The blob detection and tracking algorithm deduces the direction of an occupant and finally increments or decrements the counter. A heat signature varies between people and depends on person’s clothing. An on-board server on Raspberry Pi distributes the data via Wi-Fi to any client device in the net. The complete system includes also wireless PIR-sensors. The low-resolution IR occupancy counter has been compared with counters based on different technologies. The benefits of a low-resolution IR-imager are privacy preservation, operation capability in total darkness, energy-efficient passive operation and a low price.

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An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World Environments

Cited by 7 publications

References 28 publications

RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking

RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking

Visual Object Tracking on Multi-modal RGB-D Videos: A Review

A Low-Resolution IR-Array as a Doorway Occupancy Counter in a Smart Building

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