People Tracking Using a Time-of-Flight Depth Sensor

Bevilacqua, Alessandro; Stefano, Luigi Di; Azzari, Pietro

doi:10.1109/avss.2006.92

Cited by 69 publications

(41 citation statements)

References 8 publications

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“…(See study [116] for a recent survey of depth sensing for 3D television.) ToF cameras are applied in various tasks related to recognition and tracking of people [40,7,64] and parts of human body: hand [79,91], head [35] and face [91,108]. Alenya et al [1] use colour and ToF camera data to build 3D models of leaves for automated plant measurement.…”

Section: Time-of-flight Camerasmentioning

confidence: 99%

Image-guided ToF depth upsampling: a survey

Eichhardt

Chetverikov

Jankó

2017

Machine Vision and Applications

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Recently, there has been remarkable growth of interest in the development and applications of Time-of-Flight (ToF) depth cameras. However, despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies.

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Section: Time-of-flight Camerasmentioning

confidence: 99%

Image-guided ToF depth upsampling: a survey

Eichhardt

Chetverikov

Jankó

2017

Machine Vision and Applications

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“…In particular, while some researchers report good results with the use of low-resolution gray-scale or 3D images [10], [11] 3D video sensors generally provide better performance [10], [11], [12]. We chose a 3D sensor that produces a stream of images of 160x120 4-bit pixels at 25 frames per second [11].…”

Section: The Applicationmentioning

confidence: 99%

“…We chose a 3D sensor that produces a stream of images of 160x120 4-bit pixels at 25 frames per second [11]. As in [10], [11], [13] we developed our application in a modular fashion based on a static dataflow model of computation: each video stream is processed by a sequence of tasks AS = {A V S , A M D , A 1 , A 2 , A 3 , A 4 , A 5 , A OC }, which perform the following computations ( Fig. 1):…”

Section: The Applicationmentioning

confidence: 99%

A case study in distributed deployment of embedded software for camera networks

Leonardi¹,

Pinto²,

Carloni³

2009

2009 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition

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Abstract-We present an embedded software application for the real-time estimation of building occupancy using a network of video cameras. We analyze a series of alternative decompositions of the main application tasks and profile each of them by running the corresponding embedded software on three different processors. Based on the profiling measures, we build various alternative embedded platforms by combining different embedded processors, memory modules and network interfaces. In particular, we consider the choice of two possible network technologies: ARCnet and Ethernet. After deriving an analytical model of the network costs, we use it to complete an exploration of the design space as we scale the number of video cameras in an hypothetical building. We compare our results with those obtained for two real buildings of different characteristics. We conclude discussing the results of our case study in the broader context of other camera-network applications.

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“…A fast multi-person tracking approach in 3-D environments is Shape from Silhouettes (SfS) [6]people using few cameras. Another proposal on real-time multiperson tracking algorithm is presented in [1]. This work delves into partial occlusions and close interactions between several people under severe low-lighting conditions.…”

Section: Introductionmentioning

confidence: 99%