Vehicle detection by edge-based candidate generation and appearance-based classification

Song, Gwang Yul; Lee, Ki Yong; Lee, Joon Woong

doi:10.1109/ivs.2008.4621139

Cited by 52 publications

(25 citation statements)

References 6 publications

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“…In the hypothesis generation stage, a quick search is performed throughout the image so that only a small subset of regions likely containing vehicles are retained. The search is typically based on some expected feature of vehicles, such as color [1], shadow [2], vertical edges [1], [3], or motion [4].…”

Section: Introductionmentioning

confidence: 99%

HOG-like gradient-based descriptor for visual vehicle detection

Arróspide

Salgado

Marinas

2012

2012 IEEE Intelligent Vehicles Symposium

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Abstract-One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

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Section: Introductionmentioning

confidence: 99%

HOG-like gradient-based descriptor for visual vehicle detection

Arróspide

Salgado

Marinas

2012

2012 IEEE Intelligent Vehicles Symposium

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“…Future research directions have been identified for monocular vision based approach and ground plane detection phases of obstacle detection process. The following research directions have been identified from the existing literature [Ulrich et al [2]; Michels et al [6]; Yamaguchi et al [3]; Song et al [7]; Zhan et al [8]; Muller et al [9]; Viet et al [10]; Lin et al [11]; Cherubini et al [12]; Lim et al [13]; Mishra et al [ …”

Section: Future Research Directionsmentioning

confidence: 99%

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Singh¹,

Kaur²

2017

IJIGSP

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Abstract-Obstacle detection is the process in which the upcoming objects in the path are detected and collision with them is avoided by some sort of signalling to the visually impaired person. In this review paper we present a comprehensive and critical survey of Image Processing techniques like vision based, ground plane detection, feature extraction, etc. for detecting the obstacles. Two types of vision based techniques namely (a) Monocular vision based approach (b) Stereo Vision based approach are discussed. Further types of above described approaches are also discussed in the survey. Survey discusses the analysis of the associated work reported in literature in the field of SURF and SIFTS features, monocular vision based approaches, texture features and ground plane obstacle detection.

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“…The first usually involves a rapid search, so that the image regions that do not match an expected feature of the vehicle are disregarded, and only a small number of regions potentially containing vehicles are further analyzed. Typical features include edges [1], color [2,3], and shadows [4]. Many techniques based on stereovision have also been proposed (e.g., [5,6]), although they involve a number of drawbacks compared to monocular methods, especially in terms of cost and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Video analysis-based vehicle detection and tracking using an MCMC sampling framework

Arróspide

Salgado

Nieto

2012

EURASIP J. Adv. Signal Process.

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This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is defined. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

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Vehicle detection by edge-based candidate generation and appearance-based classification

Cited by 52 publications

References 6 publications

HOG-like gradient-based descriptor for visual vehicle detection

HOG-like gradient-based descriptor for visual vehicle detection

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Video analysis-based vehicle detection and tracking using an MCMC sampling framework

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