An integrated system for vehicle tracking and classification

Battiato, Sebastiano; Farinella, Giovanni Maria; Furnari, Antonino; Puglisi, Giovanni; Snijders, Anique; Spiekstra, Jelmer

doi:10.1016/j.eswa.2015.05.055

Cited by 48 publications

(19 citation statements)

References 24 publications

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“…In [149], using lane and vehicle information, a maneuvering target was tracked by Radar and image-sensorbased measurement. In [150], an integrated system for vehicle tracking and classification was presented. Table VI highlights key representative works in vehicle detection and tracking.…”

Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

View full text Add to dashboard Cite

show abstract

“…In recent years, the technology involved in remote sensing and object recognition has considerably advanced [31,32], with diverse applications ranging from recognition and vehicle classification [33] to the facial recognition of individuals [34]. Studies on detection and object recognition can be classified into two categories: keypoint-based object detection [35] and hierarchical and cascaded classifications [36].…”

Section: Htm Methodologymentioning

confidence: 99%

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Perea-Moreno

Ureña

Larriva

et al. 2016

Water

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Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

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“…In Shen and Miao (2014) , the tracklets are defined by using the Gaussian mixture model-based spatio-temporal features of objects in few consecutive frames and the association of reliable tracklets are used for object tracking. Battiato et al (2015) reported a tracking-by-detection method wherein a supervised patch-based learning algorithm has been employed for classifying a vehicle into one of two-types followed by the multicorrelation-based template matching for tracking the vehicle. Kim et al (2015) have integrated the MHT algorithm in a tracking-by-detection framework, which incorporates long-term appearance models of vehicles represented by features obtained from deep convolution neural network.…”

Section: Introductionmentioning

confidence: 99%