Advance in vision-based driver assistance

Klette, Reinhard; Ahn, Je; Haeusler, Ralf; Herman, Simon; Huang, Jinsheng; Khan, Waqar A.; Manoharan, Sathiamoorthy; Morales, Sandino; Morris, John; Nicolescu, Radu; Ren, Feixiang; Schauwecker, Konstantin; Xi, Yang

doi:10.1109/icetce.2011.5775884

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…The results are shown in Table 7 and compared with recent vehicle detection methods, such as Klette and Ahn's DAS, 30 Liu and Su's DAS systems 31 and Le Meur et al spatio-temporal model. 36 In Table 6, we give our comparisons by a quantitative way.…”

Section: Performance Evaluation With Other State-of-the-art Models Onmentioning

confidence: 96%

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Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Yue

2015

Int. J. Patt. Recogn. Artif. Intell.

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The driver-assistance system (DAS) becomes quite necessary in-vehicle equipment nowadays due to the large number of road tra±c accidents worldwide. An e±cient DAS detecting hazardous situations robustly is key to reduce road accidents. The core of a DAS is to identify salient regions or regions of interest relevant to visual attended objects in real visual scenes for further process. In order to achieve this goal, we present a method to locate regions of interest automatically based on a novel adaptive mean shift segmentation algorithm to obtain saliency objects. In the proposed mean shift algorithm, we use adaptive Bayesian bandwidth to¯nd the convergence of all data points by iterations and the k-nearest neighborhood queries. Experiments showed that the proposed algorithm is e±cient, and yields better visual salient regions comparing with ground-truth benchmark. The proposed algorithm continuously outperformed other known visual saliency methods, generated higher precision and better recall rates, when challenged with natural scenes collected locally and one of the largest publicly available data sets. The proposed algorithm can also be extended naturally to detect moving vehicles in dynamic scenes once integrated with top-down shape biased cues, as demonstrated in our experiments.

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Section: Performance Evaluation With Other State-of-the-art Models Onmentioning

confidence: 96%

“…Comparison of CPU running time (in seconds) for proposed methods and other systems.Klette and Ahn's30 Liu and Su 31 Le Meur et al36 Proposed method…”

mentioning

confidence: 99%

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Yue

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…Driver-assistance technologies are the subjects of widespread research and development in the car industry, especially computer-vision-based driver assistance [1]. Computer-vision-based driver monitoring is aimed mainly at detecting sleepiness and distraction using a camera mounted in front of the driver (e.g., on the dashboard).…”

Section: Introductionmentioning

confidence: 99%

Novel Backprojection Method for Monocular Head Pose Estimation

Ju¹,

Shin²,

Klette³

2013

International Journal of Fuzzy Logic and Intelligent Systems

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Estimating a driver's head pose is an important task in driver-assistance systems because it can provide information about where a driver is looking, thereby giving useful cues about the status of the driver (i.e., paying proper attention, fatigued, etc.). This study proposes a system for estimating the head pose using monocular images, which includes a novel use of backprojection. The system can use a single image to estimate a driver's head pose at a particular time stamp, or an image sequence to support the analysis of a driver's status. Using our proposed system, we compared two previous pose estimation approaches. We introduced an approach for providing ground-truth reference data using a mannequin model. Our experimental results demonstrate that the proposed system provides relatively accurate estimations of the yaw, tilt, and roll angle. The results also show that one of the pose estimation approaches (perspective-n-point, PnP) provided a consistently better estimate compared to the other (pose from orthography and scaling with iterations, POSIT) using our proposed system.

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“…These applications locate objects in a video stream and label them with information associated with the objects. For example, an application that helps a user assemble furniture can pinpoint and label each piece [53,6], a driver assistance application that locates and labels road signs can improve driver safety [28], a tourist application that recognizes landmarks and buildings can improve user experience, and so on. Researchers have also proposed applications of object recognition for smart homes [45,42] and perceptual user interfaces [54,36].…”

Section: Introductionmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

279

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Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers objectrecognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. Categories and Subject Descriptors

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Advance in vision-based driver assistance

Cited by 6 publications

References 24 publications

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

Novel Backprojection Method for Monocular Head Pose Estimation

Glimpse

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