Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition

Hsieh, Jun-Wei; Chen, Li-Chih; Chen, Duan-Yu

doi:10.1109/tits.2013.2294646

Cited by 174 publications

(84 citation statements)

References 24 publications

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“…There are however approaches where reported success rate is higher than in case of the iCamera system, but either number of predicted models is much less, as in [43] and [60] or other conditions, e.g., in which test images were acquired, make the classification task easier, as in [57]. Compared to the architecture presented in this paper, the MMR system described in [34] reports a better average success rate which is over 98 %. It is worth to notice however that the time required to complete the MMR task is for this approach about 10 ms longer than for the iCamera system in the case of 17 different car models.…”

Section: System Efficiencymentioning

confidence: 70%

“…A simple matching algorithm, where SIFT descriptors computed for a given query image are matched directly, one by one, with descriptors determined for each of the reference images, is presented in [21]. This and other reports confirm that approaches based on SIFT [80] or the speeded-up robust features (SURF) method [34] are also promising for solving the MMR problem.…”

Section: Literature Reviewmentioning

confidence: 77%

See 1 more Smart Citation

A smart camera for the surveillance of vehicles in intelligent transportation systems

Baran

Ruść

Fornalski

2015

Multimed Tools Appl

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The paper presents a smart camera aimed at security and law enforcement applications for intelligent transportation systems. An extended background is presented first as a scholar literature review. The smart camera components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of the system efficiency, are described and discussed in detail in subsequent sections. Smart features of make and model recognition (MMR), license plate recognition (LPR) and color recognition (CR) are highlighted as the main benefits of the system. Their implementations, flowcharts and recognition rates are described, discussed and finally reported in detail. In addition to MMR, three different approaches, referred to as bag-of-features, scalable vocabulary tree and pyramid match, are also considered. The conclusion includes a discussion of the smart camera system efficiency as a whole, with an insight into potential future improvements.

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Section: System Efficiencymentioning

confidence: 70%

Section: Literature Reviewmentioning

confidence: 77%

A smart camera for the surveillance of vehicles in intelligent transportation systems

Baran

Ruść

Fornalski

2015

Multimed Tools Appl

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“…Several feature-based approaches have been proposed in the literature to detect symmetries in images for object detection and segmentation [34][35][36]. The common process in these approaches is that they dedicate the design of the reliable features for patch correspondences.…”

Section: Related Workmentioning

confidence: 99%

“…The common process in these approaches is that they dedicate the design of the reliable features for patch correspondences. For instance, Hsieh et al designed a symmetric transformation to provide a framework for finding pairs of symmetric patches in vehicle images [36]. A recent survey of the symmetry in 3D geometry can be found in [37].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

Cheng

Huang

2015

Symmetry

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Abstract:In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

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“…Different strategies for vehicle detection and vehicle queue extraction are derived depending on characteristics of the input data. (Leitloff et al, 2010) (Sun et al, 2002, Sivaraman and Trivedi, 2011, Hsieh et al, 2014. (Cheng et al, 2012)utilized a color transform to separate cars from non-cars while preserving shape moment for adjusting the thresholds of the canny edge detector automatically.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection of Aerial Image Using Tv-L1 Texture Decomposition

Wang¹,

Wang²,

Li³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 × 3496 aerial image. It is promising for large-scale transportation management and planning.

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Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition

Cited by 174 publications

References 24 publications

A smart camera for the surveillance of vehicles in intelligent transportation systems

A smart camera for the surveillance of vehicles in intelligent transportation systems

Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

Vehicle Detection of Aerial Image Using Tv-L1 Texture Decomposition

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