CarSafe app

You, Chuang-Wen; Torresani, Lorenzo; Campbell, Andrew T.; Lane, Nicholas D.; Chen, Fanglin; Wang, Rui; Chen, Zhenyu; Bao, Thomas J.; Montes-de-Oca, Martha; Cheng, Yuting; Lin, Mu

doi:10.1145/2462456.2466711

Cited by 46 publications

(6 citation statements)

References 1 publication

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“…Vehicle detection has been conducted using image processing, as well as alternatives to radar and LiDAR by using local binary patterns (LBP) and Haar-like features to train AdaBoost classifiers [9]. Monocular vision avenues have been investigated, detecting pedestrians and vehicles, using Haar-like features and "cascaded" weak classifiers with distance estimation [10]. In [11], stereo vision involved two cameras carrying out 3D reconstruction, in such a way that the cameras were calibrated to compute extrinsic parameters to calculate the necessary coordinate system in front of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…Smartphone-embedded hardware and cost-efficiency benefits have been used to address these driving behaviours [10,[30][31][32][33]. Algorithms are used to detect driving events while using the front camera of the smartphone to monitor the driver's facial expressions and gestures; intervention can be triggered before an accident occurs [34].…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms are used to detect driving events while using the front camera of the smartphone to monitor the driver's facial expressions and gestures; intervention can be triggered before an accident occurs [34]. CarSafe was the first driver safety smartphone application on smartphones to use dual cameras where the front camera was used for monitoring the driver and the rear camera to detect the following distance and lane drifting [10].…”

Section: Introductionmentioning

confidence: 99%

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An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems

Fourie

Myburgh

2022

Sensors

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Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems

Fourie

Myburgh

2022

Sensors

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“…This is enabling a plethora of exciting pervasive computing applications such as the ability to record a first-person visual diary of our daily lives (i.e. lifelogging) [1], improving dietary health [2], improving driving safety [3], among many others.…”

Section: Introductionmentioning

confidence: 99%

QuickFind: Fast and contact-free object detection using a depth sensor

Zhong

Kanhere

Chou

2016

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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Pervasive computing applications have long used contact free object detection to process images collected from smartphone, tablet and wearable cameras. Two key challenges encountered in uncontrolled environments are the detection accuracy and speed, more so on computationally limited embedded systems. Recent technological advances have led to the emergence of depth sensors being integrated in a variety of embedded devices such as smartphones and wearables. Depth sensors are contact free and avoid some of the pitfalls caused by variations in lighting and background conditions that are typically associated with images from conventional cameras. This paper presents QuickFind, a fast and lightweight algorithm for object detection using only depth data. QuickFind is fast and particularly suited to run on embedded platforms as it uses a quick method for segmentation and makes use of low overhead features. We demonstrate empirically the performance of QuickFind by benchmarking on two embedded systems: Raspberry Pi and Intel Edison. We also demonstrate the wide applicability of QuickFind by developing two proof-of-concept pervasive computing applications.

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“…When it comes to interacting with humans, for example, computers can already use pretty much anything as a touchpad [2]. Similarly, when it comes to sensing the environment, computers can unobtrusively detect anything from a driver fatigue [3] to the presence of the bee queen in a hive [4]. When compared with these, advancements on the "core" frontthe computing itselfappear to be rather orthodox and limited.The last decade instilled a particularly strong driver that reshapes the way we reason about the computing ecosystem.…”

mentioning

confidence: 99%

In Search of an Accuracy-Tuneable Accelerator Platform for Ubiquitous Computing

Pilipović

Pejović

Machidon

2023

GetMobile: Mobile Comp. and Comm.

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Paving the way towards the realization of Mark Weiser's vision of ubiquitous computing [1], the research community has made incredible advancements on several fronts. When it comes to interacting with humans, for example, computers can already use pretty much anything as a touchpad [2]. Similarly, when it comes to sensing the environment, computers can unobtrusively detect anything from a driver fatigue [3] to the presence of the queen bee in a hive [4]. When compared with these, advancements on the "core" front - the computing itself - appear to be rather orthodox and limited.

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CarSafe app

Cited by 46 publications

References 1 publication

An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems

An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems

QuickFind: Fast and contact-free object detection using a depth sensor

In Search of an Accuracy-Tuneable Accelerator Platform for Ubiquitous Computing

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