An Experimental Study on Pedestrian Classification

Munder, S.; Gavrila, Dariu M.

doi:10.1109/tpami.2006.217

Cited by 506 publications

(369 citation statements)

References 13 publications

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“…In this sense, the choice of the feature extractors, histogram of oriented gradients (HOG) and local receptive fields (LRF), was motivated by the studies found in , Munder and Gavrila (2006) and Szarvas et al (2006). presented an experimental analysis demonstrating that HOG features outperforms PCA-SIFT, Haar wavelets and shape contexts in a complex data set.…”

Section: Example Of Detection By Classifier Ensemblementioning

confidence: 99%

Awareness of Road Scene Participants for Autonomous Driving

Petrovskaya

Perrollaz

Oliveira

et al. 2012

Handbook of Intelligent Vehicles

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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

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Section: Example Of Detection By Classifier Ensemblementioning

confidence: 99%

Awareness of Road Scene Participants for Autonomous Driving

Petrovskaya

Perrollaz

Oliveira

et al. 2012

Handbook of Intelligent Vehicles

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“…To illustrate that SCA captures the variability in the data in a substantially different way than a mixture of PIMs (MPIM, of the same complexity, we considered the Daimler dataset (Munder and Gavrila 2006). The full dataset is composed by 10200 gray levels images at a resolution of 18 × 36 pixels.…”

Section: A Comparison Between Sca's Components and The Centers Of A Mmentioning

confidence: 99%

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

et al. 2012

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pp. 756-763, 2005); they are often referred as "stel models." Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios.In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks. We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.

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“…[1]- [4], [7], [13], [17]- [19] Local features are extracted from training dataset and a classifier can be obtained by using some training method, such as support vector machine [20], or some boosting methods [21]. HOG feature in [1] is one of the most efficient features recently.…”

Section: Related Workmentioning

confidence: 99%

“…Haar-like feature is proposed in [13], integral image is calculated for acceleration and cascade structure is used, which is widely used in many object detection methods. Local Receptive Fields (LRF) feature [17] and Haar wavelets feature [19] are also used in human detection. Learning based method is efficient for human detection.…”

Section: Related Workmentioning

confidence: 99%

Accurate Human Detection by Appearance and Motion

Tang

Goto

2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications. key words: human detection, multi scale block histogram of template, Graphics process unit

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An Experimental Study on Pedestrian Classification

Cited by 506 publications

References 13 publications

Awareness of Road Scene Participants for Autonomous Driving

Awareness of Road Scene Participants for Autonomous Driving

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Accurate Human Detection by Appearance and Motion

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