This paper presents a comparative analysis of different pedestrian dataset characteristics. The main goal of the research is to determine what characteristics are desirable for improved training and validation of pedestrian detectors and classifiers. The work focuses on those aspects of the dataset which affect classification success using the most common boosting methods.Dataset characteristics such as image size, aspect ratio, geometric variance and the relative scale of positive class instances (pedestrians) within the training window form an integral part of classification success. This paper will examine the effects of varying these dataset characteristics with a view to determining the recommended attributes of a high quality and challenging dataset. While the primary focus is on characteristics of the positive training dataset, some discussion of desirable attributes for the negative dataset is important and is therefore included. This paper also serves to publish our current pedestrian dataset in various forms for non-commercial use by the scientific community. We believe the published dataset to be one of the largest, most flexible, and representative datasets available for pedestrian/person detection tasks.
In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data.
This paper demonstrates a method of increasing the quality of weak classifiers in the boosting context by using improved response modelling. The new method improves upon the results of a recent response binning approach proposed by Rasolzadeh et al. [1]. For experimental purposes the improved method is applied to the familiar Haar features as used by Viola and Jones in their face/pedestrian detection systems. However, the methods benefits are general and therefore not restricted to this particular feature type. Unlike many previous methods, this method is suitable for modelling multi-modal responses and is highly resistant to overfitting. It does this by adaptively choosing suitable support regions around the values taken by the standard response binning method. More accurate models are produced, with particular improvement around the final decision boundary. It is shown that the new method can be trained with one tenth of the training data required to achieve similar results on previous methods. This substantially lowers the overall training time of the system. The method's ability to consistently produce better hypotheses over a variety of pedestrian detection tasks is shown.
Abstract-This paper presents a fast Histogram of Oriented Gradients (HOG) based weak classifier that is extremely fast to compute and highly discriminative. This feature set has been developed in an effort to balance the required processing and memory bandwidth so as to eliminate bottlenecks during run time evaluation. The feature set is the next generation in a series of features based on a novel precomputed image for HOG based features. It contains features which are more balanced in terms of processing and memory requirements than its predecessors, has a larger and richer feature space, and is more discriminant on a per feature basis.In terms of computational complexity it is a heterogeneous feature set. I.e. it has fast and slow variants. In order to optimize our feature selections between the faster and slower features available we implement a recently proposed modification to the RealBoost feature selection rule. This modification provides an additional means to balance processing and memory bandwidth on ordinary PC architectures. This feature set is suitable for use within typical boosting frameworks. It is compared to Haar and Rectangular HOG features, as well the related feature HistFeat. The new feature set contains two variants, LiteHOG and LiteHOG+, which we compare. Both LiteHOG and LiteHOG+ features show promising results on road sign and pedestrian detection tasks.
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