INRIA Dataset Research Articles

A novel shape-based non-redundant local binary pattern descriptor for object detection

Motivated by the discriminative ability of shape information and local patterns in object recognition, this paper proposes a window-based object descriptor that integrates both cues. In particular, contour templates representing object shape are used to derive a set of so-called key points at which local appearance features are extracted. These key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way. At each of the extracted key points, a new local appearance feature, namely non-redundant local binary pattern (NR-LBP), is computed. An object descriptor is formed by concatenating the NR-LBP features from all key points to encode the shape as well as the appearance of the object. The proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used MIT and INRIA datasets. The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors.

A Pedestrian Detection Method Based on the HOG-LBP Feature and Gentle AdaBoost

In this paper, we present a pedestrian detection method based on the combination of Histograms of Oriented Gradient (HOG) feature and uniform local binary pattern (LBP) feature, which can detect pedestrian accurately. To the problem of low recognition rate for a single feature, we combine contour information and texture information, and propose the cascade of the two types of features, HOG features and LBP features as the feature set. In order to compare the experimental results, Gentle AdaBoost is used to train the pedestrian classifier on the INRIA dataset. The experimental results show that these two features of pedestrian detection algorithm improve the accuracy and reduce the error rate. Our method achieve a detection rate of 94.05% at FPPW = 10 -4 , which is better than Dalal’s (detection rate of 84% to 89% at 10 -4 FPPW).

Real-time human detection based on gentle MILBoost with variable granularity HOG-CSLBP

Sample misalignment exerts an important influence on training a rapid and accurate human detector, and it is a difficult problem to tackle with due to human articulation or manual annotation errors. Multiple instances learning method is an effective tool to deal with this difficulty without manual correction. In this paper, firstly, we propose a variable granularity HOG-CSLBP feature, which combines the human shape information with local texture information, and encodes spatial relationship in different granularity to improve its discriminative ability. Our new feature takes an advantage of the mutual complementarities of histogram of gradient and center-symmetric local binary patterns feature, which is adept at modeling human. Secondly, we present a Gentle MILBoost algorithm which utilizes the Newton update technique to get an optimal weak classifier that is able to discriminate complex distribution and is more stable in numerical computation. Experimental results based on INRIA, MIT-CBCL and TUD-Brussels datasets have showed superior performance of our method. Moreover, our method can achieve real-time speed in real application.

A robust pedestrian detection approach based on shapelet feature and Haar detector ensembles

Detection of pedestrians in images and video sequences is important for many applications but is very challenging due to the various silhouettes of pedestrians and partial occlusions. This paper describes a two-stage robust pedestrian detection approach. The first stage uses a full body detector applied to a single image to generate pedestrian candidates. In the second stage, each pedestrian candidate is verified with a detector ensemble consisting of part detectors. The full body detector is trained based on improved shapelet features, while the part detectors make use of Haar-like wavelets as features. All the detectors are trained by a boosting method. The responses of the part detectors are then combined using a detector ensemble. The verification process is formulated as a combinatorial optimization problem with a genetic algorithm for optimization. Then, the detection results are regarded as equivalent classes so that multiple detections of the same pedestrian are quickly merged together. Tests show that this approach has a detection rate of over 95% for 0.1% FPPW on the INRIA dataset, which is significantly better than that of the original shapelet feature based approach and the existing detector ensemble approach. This approach can robustly detect pedestrians in different situations.

Pedestrian detection in images via cascaded L1-norm minimization learning method

A new cascaded L1-norm minimization learning (CLML) method for pedestrian detection in images is proposed in this paper. The proposed CLML method, which is designed from the perspective of Vapnic's theory in the statistical learning, integrates feature selection with classifier construction via solving meaningful optimization models. The method incorporates three stages: weak classifier learning, strong classifier learning and the cascaded classifier construction. In the weak classifier learning, the L1-norm minimization learning (LML) and min-max penalty function model are presented. In the strong classifier learning, an integer programming optimization model is built, equaling the reformulation of LML in the integer space. Finally, a cascade of LML classifiers is constructed to promote detection speed. During the classifier learning and pedestrian detection, Histograms of Oriented Gradients of variable-sized blocks (v-HOG) are used as feature descriptors. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance and speed than the state-of-the-art methods.

Efficient HOG human detection

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans—more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method.

Histogram of Template for Pedestrian Detection

n this paper, we propose a novel feature named histogram of template (HOT) for human detection in still images. For every pixel of an image, various templates are defined, each of which contains the pixel itself and two of its neighboring pixels. If the texture and gradient values of the three pixels satisfy a pre-defined formula, the central pixel is regarded to meet the corresponding template for this formula. Histograms of pixels meeting various templates are calculated for a set of formulas, and combined to be the feature for detection. Compared to the other features, the proposed feature takes texture as well as the gradient information into consideration. Besides, it reflects the relationship between 3 pixels, instead of focusing on only one. Experiments for human detection are performed on INRIA dataset, which shows the proposed HOT feature is more discriminative than histogram of orientated gradient (HOG) feature, under the same training method.

Orientation filter enhanced pedestrian detection

Presented is a novel method that can detect pedestrians in images based on the combination of AdaBoost learning with a local histogram's features. Instead of using the raw image for further processing, introduced is a layer enhanced by orientation filters which are superimposed to the original image. Experimental results obtained using the INRIA dataset show the superior performance of the presented method and thus demonstrate its robustness with the novel enhanced layer embedded pedestrian detector.

Accurate Human Detection by Appearance and Motion

In 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.

Pedestrian Detection via Classification on Riemannian Manifolds

We present a new algorithm to detect pedestrian in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well known machine learning techniques are not well suited to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. The main contribution of the paper is a novel approach for classifying points lying on a connected Riemannian manifold using the geometry of the space. The algorithm is tested on INRIA and DaimlerChrysler pedestrian datasets where superior detection rates are observed over the previous approaches.