Efficient Resource Allocation for Attentive Automotive Vision Systems

Matzka, Stephan; Wallace, Andrew M.; Pétillot, Yvan

doi:10.1109/tits.2011.2182610

Cited by 24 publications

(19 citation statements)

References 18 publications

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“…A similar system was presented in [14] and [176]. Saliency was used as the vision cue in [177], which was fused with lidar in a Bayesian framework. Fusion of stereo vision with lidar was performed in [178]- [181].…”

Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

“…Others detect vehicles with vision and detect range with radar or lidar [14]. Future works will need a principled object-level fusion of vision and active sensors for vehicle detection [177]. Such an information fusion could reduce estimation covariance and enhance robustness, although the asynchronous nature of the multiple modalities will need to be handled [190].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

Section: A Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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show abstract

“…As lane tracking cameras become standard options on serial production vehicles, the opportunity to integrate vision with active sensing technology will present itself, with vision providing an intuitive level of semantic abstraction. Future works will need a principled, object-level fusion of vision and radar/lidar for vehicle detection [81]. Such an information fusion could reduce estimation covariance and enhance robustness, although the asynchronous nature of the multiple modalities will need to be handled [82].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A review of recent developments in vision-based vehicle detection

Sivaraman¹,

Trivedi²

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the state-of-the-art, and provide perspective on future research directions in the field.

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“…However, input device does not support any other types than control device. Because an autonomous vehicle requires LIDAR and GPS systems [3], proposed research creates artificial LIDAR and GPS signals as essential components. Additionally, developers can add or modify original modules to their needs since our method is based on open-source project.…”

Section: Previous Workmentioning

confidence: 99%