A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-VISION image processing board

Kyo, Shorin; Koga, T.; Sakurai, K.; Okazaki, Shinichiro

doi:10.1109/itsc.1999.821095

Cited by 41 publications

(22 citation statements)

References 1 publication

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“…Many sensors are available for detecting vehicles [3], [8], [21]- [24], [35], [38], [44], such as laser radar, passive far infrared, reflected light detector, microwave radar, millimeterwave radar, acoustic array, and ultrasonic detector. Each sensor has its own advantages and disadvantages [20].…”

Section: B Sensor Overviewmentioning

confidence: 99%

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System

Cherng

Fang

Chen

et al. 2009

IEEE Trans. Intell. Transport. Syst.

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Abstract-Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway. The DVM is motivated by the human visual system and consists of three analyzers: 1) sensory analyzers, 2) perceptual analyzers, and 3) conceptual analyzers. In addition, a memory, which is called the episodic memory, is incorporated, through which a number of features of the system, including hierarchical processing, configurability, adaptive response, and selective attention, are realized. A series of experimental results with both single and multiple critical motions are demonstrated and show the feasibility of the proposed system.Index Terms-Assembly of adaptive-resonance-theory (ART) neural networks, driver-assistance system (DAS), dynamic visual model (DVM), fuzzy integral, spatiotemporal attention (STA) neural network.

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Section: B Sensor Overviewmentioning

confidence: 99%

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System

Cherng

Fang

Chen

et al. 2009

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…The second step is validation of candidate correctness (hypothesis verification) [14]. During the hypothesis generation step, various kinds of algorithms are proposed including vehicle detection using knowledge of symmetry, color, shadow, corner, vertical/horizontal edge, texture and light for vehicles [5]. Vehicle detection can also be performed by using stereo information using a disparity map, as well as motion information based on optical flow calculations.…”

Section: Variety Of Vision Applications and Vision Algorithmsmentioning

confidence: 99%

IMAPCAR: A 100 GOPS In-Vehicle Vision Processor Based on 128 Ring Connected Four-Way VLIW Processing Elements

Kyo

Okazaki

2008

J Sign Process Syst

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This paper presents IMAPCAR, a 100GOPS programmable highly parallel vision processor LSI consuming less than 2 W of power for in-vehicle vision tasks of driver assistance systems. First, requirements of vision processors for driver assistance systems as well as the characteristics of vision tasks for safety are summarized. Next, features in the design of IMAPCAR are described in detail, which comparing with a previous design, improved the performance for major vision tasks by a factor of 2.5 while reduced 50% of power. Design choices taken by other in-vehicle vision processors are also compared and analyzed. Finally, technology perspectives of future invehicle vision processors are discussed.

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“…The application used in the benchmark is a vehicle detection program described in [26]. As shown in Fig.13(a), the source image is first divides into four overlapping regions A to D, upon each of which a sequence of image recognition tasks are applied: normalization of image pixel values within the each region, (the Prepare task), edge detection (the Detect edge task), and edge segment selection based on sizes and strengths of each detected edges (the Select edge task, also refer to Fig.13(c)).…”

Section: Application Level Evaluationmentioning

confidence: 99%

An Integrated Memory Array Processor Architecture for Embedded Image Recognition Systems

Kyo¹,

Okazaki²,

Arai

2005

SIGARCH Comput. Archit. News

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A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-VISION image processing board

Cited by 41 publications

References 1 publication

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System

Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System

IMAPCAR: A 100 GOPS In-Vehicle Vision Processor Based on 128 Ring Connected Four-Way VLIW Processing Elements

An Integrated Memory Array Processor Architecture for Embedded Image Recognition Systems

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