In this paper we present an study on the importance of the grid configuration in gender recognition from whole body static images. By using a simple classifier (AdaBoost) and the well-known Histogram of Oriented Gradients features we test several grid configurations. Compared with previous approaches, which use more complicated classifiers or feature extractors, our approach outperforms them in...

We propose a human lower body pose estimation method for team sport videos, which is integrated with tracking-by-detection technique. The proposed Label-Grid classifier uses the grid histogram feature of the tracked window from the tracker and estimates the lower body joint position of a specific joint as the class label of the multiclass classifiers, whose classes correspond to the candidate j...

This paper presents a new method for human activity recognition using depth sequences. Each depth sequence is represented by three depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues. A feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is introduced to extract features from DMMs. The gradient lo...

Off-line recognition of text plays a significant role in several applications, such as cheque verification and mail sorting. However, the selection of the technique for feature extraction remains a big challenging step for achieving high recognition accuracy. This paper presents an efficient handwritten digit recognition system based on HOG to capture the discriminative features of digit image....

Pedestrian detection is an important problem in the design of driving assistance systems that can reduce accidents and save lives. Although many annotated visible pedestrian datasets are publically available, similar annotated thermal datasets are rare. Such datasets are essential for training a classifier that can be used for pedestrian detection at night from thermal images. Manual annotation...

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of squareblocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature sele...

In this paper, we present a new pedestrian detection method combining Random Forest and Dominant Orientation Templates(DOT) to achieve state-of-the-art accuracy and, more importantly, to accelerate run-time speed. DOT can be considered as a binary version of Histogram of Oriented Gradients(HOG) and therefore provides time-efficient properties. However, since discarding magnitude information, it...

The Object detection based on histogram of oriented gradients was introduced in the context of detecting humans in images [1]. Detecting human subject in the image is a difficult task due to the fact that they appear in different appearance and poses. Thus the need to come up with a feature that allows human dorm to be detected cleanly even in cluttered background under different illumination. ...

This paper describes our participation in the Visual Privacy Task of MediaEval 2013, which aims to obscure human occurrence in image sequences. As a result the recorded person should be unrecognisable. We use an approach which pseudo-randomly scrambles pixels within specified regions. This technique is reversible and preserves the colour characteristic of each region. So, colour-based approache...

Due to the increasingly need for automatic traffic monitoring, vehicle license plate detection is of high interest to perform automatic toll collection, traffic law enforcement, parking lot access control, among others. In this paper, a sliding window approach based on Histogram of Oriented Gradients (HOG) features is used for Brazilian license plate detection. This approach consists in scannin...