Efficient pedestrian detection by directly optimizing the partial area under the ROC curve

Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). Effective cascade-based classification, for example, depends on training node classifiers that achieve the maximal detection rate at a moderate false positive rate, e.g., around 40% to 50%. We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. By optimizing for different ranges of false positive rates, the proposed method can be used to train either a single strong classifier or a node classifier forming part of a cascade classifier. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method.Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Henge

Ten Years of Pedestrian Detection, What Have We Learned?

Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark. We observe that there exist three families of approaches, all currently reaching similar detection quality. Based on our analysis, we study the complementarity of the most promising ideas by combining multiple published strategies. This new decision forest detector achieves the current best known performance on the challenging Caltech-USA dataset.Comment: To appear in ECCV 2014 CVRSUAD workshop proceeding

Sharing features in multi-class boosting via group sparsity

We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.Sakrapee Paisitkriangkrai, Chunhua Shen and Anton van den Henge

Training effective node classifiers for cascade classification

Extent: 23p. The final publication is available at www.springerlink.com: http://link.springer.com/article/10.1007/s11263-013-0608-1Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.Chunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai, Anton van den Henge

Performance evaluation of local features in human classification and detection

Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and ’Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection.S. Paisitkriangkrai, C. Shen and J. Zhan

Fast pedestrian detection using a cascade of boosted covariance features

Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally extracted features (e.g., local receptive fields, histogram of oriented gradients, and region covariance). Building upon the findings of our experiments, we propose a new, simpler pedestrian detector using the covariance features. Unlike the work in [1], where the feature selection and weak classifier training are performed on the Riemannian manifold, we select features and train weak classifiers in the Euclidean space for faster computation. To this end, AdaBoost with weighted Fisher linear discriminant analysis-based weak classifiers are designed. A cascaded classifier structure is constructed for efficiency in the detection phase. Experiments on different datasets prove that the new pedestrian detector is not only comparable to the state-of-the-art pedestrian detectors but it also performs at a faster speed. To further accelerate the detection, we adopt a faster strategy-multiple layer boosting with heterogeneous features-to exploit the efficiency of the Haar feature and the discriminative power of the covariance feature. Experiments show that, by combining the Haar and covariance features, we speed up the original covariance feature detector [1] by up to an order of magnitude in detection time with a slight drop in detection performance.Paisitkriangkrai Sakrapee, Chunhua Shen and Jian Zhan

An overview of fast pedestrian detection: feature selection and cascade framework of boosted features

Efficiently and accurately detecting pedestrians plays a crucial role in many vision applications such as video surveillance, multimedia retrieval and smart car etc. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally-extracted features. Building upon our findings, we propose a new, simpler pedestrian detecting framework based on the covariance features. We conduct feature selection and weak classifier training in the Euclidean space for faster computation. To this end, two machine learning algorithms have been designed: AdaBoost with weighted Fisher linear discriminant analysis (WLDA) based weak classifiers and Greedy Sparse Linear Discriminant Analysis (GSLDA). To further accelerate the detection, we employ a faster strategy, multiple cascade layers with heterogeneous features, to exploit the efficiency of the Haar-like features and the discriminative power of the covariance features. Experimental results shown on different datasets prove that the new pedestrian detection is not only comparable to the performance of the state-of-the-art pedestrian detectors but it also performs at a faster speed.Jian Zhang, Sakrapee (Paul) Paisitkriangkrai and Chunhua She

An experimental study on pedestrian classification using local features

This paper presents an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on both the benchmarking dataset used in [1] and the MIT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1].Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhan

Face detection from few training examples

Face detection in images is very important for many multimedia applications. Haar-like wavelet features have become dominant in face detection because of their tremendous success since Viola and Jones [1] proposed their AdaBoost based detection system. While Haar features' simplicity makes rapid computation possible, its discriminative power is limited. As a consequence, a large training dataset is required to train a classifier. This may hamper its application in scenarios that a large labeled dataset is difficult to obtain. In this work, we address the problem of learning to detect faces from a small set of training examples. In particular, we propose to use covariance features. Also for better classification performance, linear hyperplane classifier based on Fisher discriminant analysis (FDA) is proffered. Compared with the decision stump, FDA is more discriminative and therefore fewer weak learners are needed. We show that the detection rate can be significantly improved with covariance features on a small dataset (a few hundred positive examples), compared to Haar features used in current most face detection systems. © 2008 IEEE